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or published in other medical journals. Posts must contain content related to one of the journal’s categories.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The content published\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The journal is divided into 3 categories:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Scientific research article: are valuable scientific works, which have been researched and accepted.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Overview of medicine, biology and pharmacy: serving the objective of continuing training in the fields of medicine, biology and pharmacy; to systematize classical and modern knowledge.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Update information on new knowledge about medicine, biology, pharmacy in the country and in the world.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Scope\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Publication and introduction of scientific research in the fields:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Medicine (internal medicine, surgery, pediatrics, obstetrics and gynecology, odonto-stomatology, laboratory, oncology, traditional medicine, nursing).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Biology (genetics, biotechnology).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Pharmacology (pharmaceutics, drug quality analysis-control, synthetic pharmaceutical chemistry, biochemistry, pharmacognosy, botany, clinical pharmacy).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- To enhance the quality of undergraduate, postgraduate education, scientifically researching and meet the necessary treatment in hospital.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Introducing the updated domestic and oversea information about science technology to promote scientific research and exchanging technology in local, other universities.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Exchanging pharmaceutical and medical information for social health developing in the Mekong Delta and Vietnam.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The object\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Postgraduate students, student of Can Tho University of Medicine and Pharmacy, scientists from schools, research institutes, hospitals, health centers, pharmaceutical companies of the Mekong Delta; other provinces and regions in Vietnam and other country.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Address\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Headquarters of Can Tho Journal of Medicine and Pharmacy, located Scientific Research and International Cooperation Office: 179 Nguyen Van Cu Street, An Khanh Ward, Ninh Kieu District, Can Tho City, Vietnam.\u003C\u002Fspan>\u003C\u002Fp>","\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Ngày 16\u002F7\u002F2015, Tạp chí Y Dược học Cần Thơ được cấp chỉ số quốc tế: ISSN 2354-1210.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 4\u002F2016, Tạp chí đã được Hội đồng Giáo sư ngành Y đưa vào danh sách các tạp chí khoa học Y học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Năm 2020 Tạp chí Y Dược học Cần Thơ đã được phê duyệt vào danh mục của các Hội đồng Giáo sư ngành Dược học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ ra 12 số\u002Fnăm, 180-200 trang\u002Fsố.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 12\u002F2022 Tạp chí Y Dược học Cần Thơ là thành viên của hệ thống Crossref và từ tháng 01\u002F2023 tạp chí thực hiện bình duyệt online kín 2 chiều nhằm tăng tính minh bạch, tin cậy của các công trình nghiên cứu khoa học và đảm bảo tốt nhất chất lượng khoa học của bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ, mục đích và phạm vi của tạp chí\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ và mục đích hoạt động của tạp chí: xuất bản nhằm mục đích phổ biến kết quả từ các đề tài nghiên cứu khoa học; giao lưu trao đổi khoa học, chia sẻ kinh nghiệm, học tập, đồng thời cập nhật thông tin khoa học mới trong các lĩnh vực y, sinh, dược học trong và ngoài nước.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phạm vi của tạp chí: Tạp chí xuất bản được chia thành 3 chuyên mục: (i) Bài báo nghiên cứu khoa học là kết quả công trình nghiên cứu khoa học có giá trị đã được triển khai nghiên cứu, (ii) Bài tổng quan y, sinh, dược học: phục vụ mục tiêu đào tạo liên tục trong lĩnh vực y, sinh, dược học; nhằm hệ thống hóa những kiến thức kinh điển và hiện đại; (iii) Thông tin cập nhật kiến thức mới về y, sinh, dược học trong nước và trên thế giới.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Chính sách truy cập mở\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ áp dụng chính sách truy cập mở đối với các bài báo đã xuất bản đến với độc giả, nhằm mở rộng cơ hội tiếp cận các kết quả nghiên cứu chất lượng cao và tăng cường trao đổi kiến thức. Tạp chí đăng tải trực tuyến (miễn phí) toàn văn các bài báo được công bố trên website của Tạp chí (https:\u002F\u002Ftapchi.ctump.edu.vn).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đạo đức xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ cam kết tuân thủ đạo đức xuất bản phù hợp với các hướng dẫn và tiêu chuẩn của the Committee on Publication Ethics (COPE), tuân thủ các nguyên tắc của COPE’s Core Practices, Best Practices Guidelines for Journal Editors và Guidelines on Good Publication Practices.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Bản thảo bài báo chỉ được chấp nhận khi được tác giả chịu trách nhiệm chính cam kết các nội dung sau: Các nội dung của bản thảo chưa được đăng tải toàn bộ hoặc một phần ở các tạp chí khác; Tất cả các tác giả đều có đóng góp một cách đáng kể vào quá trình nghiên cứu hoặc chuẩn bị bản thảo và cùng chịu trách nhiệm về các nội dung của bản thảo; Tuân thủ các biện pháp đảm bảo đạo đức nghiên cứu (ví dụ thỏa thuận đồng ý tham gia nghiên cứu).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Cam kết bảo mật\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí cam kết thực hiện và tuân thủ các quy định của luật và các văn bản hướng dẫn liên quan đến bảo mật thông tin cá nhân trên không gian mạng. Các thông tin mà người dùng (tác giả, độc giả, biên tập viên, người phản biện) nhập vào các biểu mẫu trên Hệ thống Quản lý xuất bản trực tuyến của tạp chí chỉ được sử dụng vào các mục đích đã được tuyên bố rõ ràng và sẽ không được cung cấp cho bất kỳ bên thứ ba nào khác, hay dùng vào bất kỳ mục đích nào khác.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phí gửi bài\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng bài: 1.000.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng nhanh: 1.500.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với tác giả là cán bộ viên chức thuộc Trường Đại học Y Dược Cần Thơ thì được hỗ trợ 50% lệ phí gửi đăng bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với sinh viên thực hiện đề tài nghiên cứu khoa học cấp trường được hỗ trợ 100% lệ phí đăng bài ( Tác giả gửi đính kèm “ Quyết định về việc giao tổ chức thực hiện đề tài nghiên cứu khoa học cấp Trường của sinh viên”).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Hình thức nộp lệ phí:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Tiền mặt:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Nộp trực tiếp tại Phòng Tài chính - Kế toán, Trường Đại học Y Dược Cần Thơ, số 179 Nguyễn Văn Cừ, P. An Khánh, Q. Ninh Kiều, thành phố Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Chuyển khoản:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tên Tài khoản: Trường ĐHYD Cần Thơ, Số TK: 0111000115668, tại ngân hàng Vietcombank chi nhánh Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Thời gian: Áp dụng từ ngày 01\u002F02\u002F2023.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">* Phí gửi bài không được hoàn trả khi bài viết bị từ chối hoặc tác giả xin rút bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Quy trình phản biện bài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ thực hiện quy trình phản biện kín hai chiều nghiêm ngặt. Danh tính của những người phản biện không được tiết lộ cho các tác giả và ngược lại. Quy trình thẩm định bài báo đăng gồm các bước sau:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tiếp nhận bản thảo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tác giả liên hệ gửi bản thảo đến Tạp chí qua hệ thống trực tuyến tại website: https:\u002F\u002Ftapchi.ctump.edu.vn. Hướng dẫn về cách đăng ký, gửi bài và chuẩn bị bản thảo được cung cấp trên website của Tạp chí.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sàng lọc sơ bộ\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sau khi Tòa soạn nhận được bài báo của tác giả, Ban Thư ký sẽ tiến hành kiểm tra sơ bộ bài báo (các yêu cầu về nội dung và hình thức). Những bài báo không đúng quy cách hoặc có nội dung không phù hợp hoặc vi phạm bản quyền sẽ bị từ chối (Ban Thư ký thông báo phản hồi đến tác giả trong vòng 1 tuần). Những bài báo đủ điều kiện, được Ban Thư ký tòa soạn chuyển đến Ban Biên tập có cùng chuyên môn với nội dung bài báo để đề xuất người phản biện. Thời gian kể từ khi Ban Biên tập nhận bài báo đến khi đề xuất người phản biện bài báo chậm nhất là 5 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Vòng phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký gửi bài và yêu cầu phản biện đến 02 phản biện độc lập.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Các phản biện gởi nhận xét cho Ban Thư ký. Thời gian từ khi gửi bài cho phản biện đến khi nhận ý kiến của phản biện tối đa là 20 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xử ký kết quả phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Nếu ý kiến đồng ý cho đăng và không cần chỉnh sửa, Ban Thư ký tiếp tục đăng bài theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Nếu ý kiến đồng ý đăng và cần chỉnh sửa, Ban Thư ký sẽ thông tin đến tác giả chỉnh sửa theo yêu cầu của người phản biện. Thời gian chỉnh sửa và gửi lại kéo dài không quá 2 tuần, từ khi tác giả bài báo nhận được thông tin (Quá trình này có thể lặp lại tối đa 2 lần\u002F1 bài báo). Khi có sự thống nhất, đồng ý của người phản biện; bài báo được tiếp tục đăng theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Những bài báo có chất lượng không đạt yêu cầu, cả 2 phản biện không đồng ý cho đăng sẽ bị Tòa soạn từ chối đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký tổng hợp các bản thảo đã được tác giả hoàn thiện sau thẩm định trình Ban Biên tập xem xét, Tổng Biên tập phê duyệt, quyết định bài đăng theo các tiêu chí: sự phù hợp nội dung với tôn chỉ và mục đích, thể loại bài viết (ưu tiên các bài có bài có nghiên cứu chuyên sâu, hàm lượng khoa học cao), đóng góp mới bài báo, bài báo được ưu tiên đăng trong số gần nhất của Tạp chí theo thứ tự: tính thời sự, chất lượng bài báo và thời gian gửi bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Ban Biên tập và Ban Thư ký biên tập bản thảo, chế bản, đọc rà soát lỗi. Thời gian hoàn thành từ 10-15 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Ban Thư ký có trách nhiệm thông báo cho tác giả bài báo (bằng e-mail) về tình hình phê duyệt bài báo, thời gian, số kỳ, tập xuất bản bài báo theo qui định.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">4. Danh sách bài báo theo số Tạp chí được in ấn và phát hành trong năm định kỳ được công bố chính thức trên website: https:\u002F\u002Ftapchi.ctump.edu.vn\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>",{"VOID":606},"wcQ1uqwAAAAJ","2023-05-30T08:17:21.868+00:00",[],[610],{"id":611,"createTime":18,"updateTime":18,"relativeEntities":612,"slug":18,"properties":613,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":623,"parentIds":624,"statistic":18},"6413896b-eca9-442b-a73f-182a58a0ce40",[],{"title":614,"address":617,"country":620,"abbreviation":621},{"EN":615,"VI":616},"Can Tho University of Medicine and Pharmacy","Trường Đại học Y Dược Cần Thơ",{"EN":618,"VI":619},"No 179, Nguyen Van Cu street, An Khanh ward, Ninh Kieu district, Can Tho city, Vietnam","Số 179, đường Nguyễn Văn Cừ, phường An Khánh, quận Ninh Kiều, thành phố Cần Thơ, Việt Nam",{"VOID":178},{"VOID":622},"ctump","http:\u002F\u002Fwww.ctump.edu.vn\u002F",[],[],"https:\u002F\u002Ftapchi.ctump.edu.vn\u002Findex.php\u002Fctump",{"impactFactor":19,"impactFactorByYear":628,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":630,"totalPublicationByYear":631,"totalCitation":635,"totalCitationByYear":636,"totalCitationPerPublication":95,"totalCitationPerPublicationByYear":638,"hindexLast5Year":205,"hindex":205},{"2022":629,"2023":88,"2024":263},0.01,1556,{"2020":145,"2021":112,"2022":632,"2023":633,"2024":634,"2025":274},306,801,358,161,{"2021":291,"2022":409,"2023":637},99,{"2021":639,"2022":447,"2023":90},0.23,{"impactFactor":18,"impactFactorByYear":18,"i10Index":275,"i10IndexLast5Year":275,"totalPublication":641,"totalPublicationByYear":642,"totalCitation":641,"totalCitationByYear":643,"totalCitationPerPublication":200,"totalCitationPerPublicationByYear":646,"hindexLast5Year":101,"hindex":101},476,{"0":339,"2019":275,"2021":108,"2022":576,"2023":568,"2024":482,"2025":101,"2026":207},{"2021":202,"2022":275,"2023":300,"2024":644,"2025":485,"2026":645},136,83,{"2021":262,"2022":629,"2023":647,"2024":279,"2025":648,"2026":649},0.62,25.43,13.83,{"id":651,"createTime":652,"updateTime":504,"relativeEntities":653,"slug":654,"properties":655,"entityType":16,"verifyStatus":188,"verifyTime":18,"verifyNote":18,"languages":667,"translateLanguages":18,"viewCount":283,"subjectFields":668,"manageAffiliations":669,"indexDatabases":670,"url":671,"thumbnailPath":672,"statistic":673,"gsStatistic":706,"type":213,"analyzePriority":18},"6984a56a-db70-403b-9cc4-4013e1ceaffa","2023-05-09T06:47:40.346+00:00",[],"T%E1%BA%A1p%20ch%C3%AD%20Nghi%C3%AAn%20c%E1%BB%A9u%20n%C6%B0%E1%BB%9Bc%20ngo%C3%A0i",{"country":656,"issn":657,"title":659,"introduce":662,"gsId":665},{"VOID":178},{"VOID":658},"25252445",{"EN":660,"VI":661},"VNU Journal of Foreign Studies","Tạp chí Nghiên cứu nước ngoài",{"EN":663,"VI":664},"{\"ops\":[{\"insert\":\"\\n\\nThe \\n\"},{\"attributes\":{\"italic\":true},\"insert\":\"VNU Journal of Science\"},{\"insert\":\"\\n was established in 1985 for the publication of national and international research papers in all fields of natural sciences and technology, social sciences and humanities. Since then, the journal has grown in quality, size and scope and now comprises a dozen of serials spanning academic research. 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Tạp chí xuất bản các bài báo gốc có giá trị khoa học hoặc công nghệ trong tất cả các lĩnh vực khoa học tự nhiên, xã hội hoặc giáo dục.\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Chuyên san Khoa học tự nhiên và công nghệ:\"},{\"insert\":\" Là các bài báo mô tả những phát hiện có giá trị trong vật lý, toán học, hóa học, sinh học; giải quyết các vấn đề kỹ thuật hoặc công nghệ.\"},{\"attributes\":{\"list\":\"bullet\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Chuyên san Khoa học Xã hội và Nhân văn:\"},{\"insert\":\" là các bài báo xuất bản chất lượng cao trong các lĩnh vực khác nhau của khoa học xã hội và nghiên cứu phát triển con người.\"},{\"attributes\":{\"list\":\"bullet\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Chuyên san Khoa học giáo dục:\"},{\"insert\":\" là các bài báo xuất bản trong lĩnh vực khoa học giáo dục và các ứng dụng của tiến bộ vào giáo dục để cải thiện và nâng cao giáo dục khoa học ở tất cả các cấp.\"},{\"attributes\":{\"list\":\"bullet\"},\"insert\":\"\\n\"},{\"insert\":\"Tạp chí trường ĐHSP Hà Nội 2 xuất bản được phản biện kín, xét duyệt bởi ít nhất 02 chuyên gia, và được đánh giá, chọn lựa từ ban biên tập và Tổng biên tập.\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Các loại bài báo\"},{\"insert\":\":\\nBài báo nghiên cứu:\"},{\"attributes\":{\"list\":\"ordered\"},\"insert\":\"\\n\"},{\"insert\":\"Báo cáo học thuật về nghiên cứu ban đầu chưa từng được xuất bản ở bất kỳ nơi nào, hay bằng bất kỳ ngôn ngữ nào khác. Bản thảo thích hợp, nên chứa các phần sau theo thứ tự: Tiêu đề, Tác giả, Liên kết tác giả, Địa chỉ email của tác giả tương ứng, Tóm tắt, Từ khóa, Danh pháp (nếu có), Giới thiệu, Thử nghiệm, Lý thuyết, Kết quả và thảo luận, Kết luận, Xung đột quan tâm, Lời cảm ơn (nếu có), Tài liệu tham khảo, Phụ lục (nếu có). Bản xuất bản trước phải được định dạng theo Mẫu (phiên bản MS-Word).\\n2. Bài báo tổng quan:\\nNgoài các bài phê bình được mời, các bài phê bình tài liệu, bài phê bình có hệ thống và bài phê bình sẽ được chấp nhận để xem xét. Bản thảo cần được soạn thảo và sắp xếp theo trình tự yêu cầu: Tên sách, Tên tác giả, Liên kết, Địa chỉ email, Tóm tắt, Từ khóa, Nội dung chính, Kết luận, Xung đột lợi ích, Lời cảm ơn (nếu có), Tài liệu tham khảo. Mặc dù, cấu trúc văn bản chính có thể thay đổi dựa trên các chủ đề phụ của bài đánh giá, các bài báo nên được định dạng theo các Mẫu phù hợp như các bài báo nghiên cứu.\\n\"}]}",{"VOID":833},"YPoBvsIAAAAJ",[],[],[],"https:\u002F\u002Fsj.hpu2.edu.vn\u002Findex.php\u002Fjournal","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F954132b5-ca74-461c-b819-45ad6e49a404\u002F2790ef1d0a7d7a40a504c2fc1647f670.jpg",{"impactFactor":19,"impactFactorByYear":840,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":133,"totalPublicationByYear":842,"totalCitation":284,"totalCitationByYear":843,"totalCitationPerPublication":639,"totalCitationPerPublicationByYear":844,"hindexLast5Year":275,"hindex":275},{"2024":841},0.17,{"2022":132,"2023":107,"2024":287},{"2022":482,"2023":278,"2024":275},{"2022":306,"2023":355,"2024":303},{"impactFactor":18,"impactFactorByYear":18,"i10Index":205,"i10IndexLast5Year":205,"totalPublication":456,"totalPublicationByYear":846,"totalCitation":295,"totalCitationByYear":847,"totalCitationPerPublication":848,"totalCitationPerPublicationByYear":849,"hindexLast5Year":206,"hindex":206},{"0":275,"2022":284,"2023":132,"2024":227,"2025":290},{"2023":206,"2024":132,"2025":121,"2026":107},1.22,{"2023":267,"2024":466,"2025":850},4.56,{"id":852,"createTime":853,"updateTime":854,"relativeEntities":855,"slug":856,"properties":857,"entityType":16,"verifyStatus":188,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":227,"subjectFields":869,"manageAffiliations":870,"indexDatabases":878,"url":918,"thumbnailPath":18,"statistic":919,"gsStatistic":950,"type":213,"analyzePriority":18},"21ccdb34-414d-420f-8a60-a592a2fa848e","2023-05-29T10:42:53.358+00:00","2026-08-27T01:57:29.560+00:00",[],"Vietnam-Journal-of-Earth-Sciences",{"country":858,"eissn":859,"issn":861,"title":863,"introduce":865,"gsId":867},{"VOID":178},{"VOID":860},"26159783",{"VOID":862},"08667187",{"EN":864},"Vietnam Journal of Earth Sciences",{"EN":866},"Science of the Earth, formerly Vietnam Journal of Earth Sciences, is a peer-reviewed journal to publish high-quality articles on the entire range of earth sciences and the environment, focused on the Asia Pacific region and their correlations and connections to the globe. The journal publishes fundamental and applied research in earth sciences and the environment, including geology, geophysics, geography, soil science, hydrology, meteorology, oceanography, petroleum, geohazards, environmental sciences, environmental engineering, sustainable development, geoinformatics, geodesy, GIS, and remote sensing.",{"VOID":868},"5htfr3YAAAAJ",[],[871],{"id":231,"createTime":18,"updateTime":18,"relativeEntities":872,"slug":18,"properties":873,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":240,"parentIds":877,"statistic":18},[],{"title":874,"country":875,"abbreviation":876},{"EN":235,"VI":236},{"VOID":178},{"VOID":239},[],[879,891,902],{"id":880,"indexDatabase":881,"url":886,"indexYears":887,"academicFieldIds":888,"indexDatabaseRanking":890},"6ace2085-a177-4a27-b309-8813b832111e",{"id":71,"createTime":18,"updateTime":18,"relativeEntities":882,"label":883,"description":884,"key":77,"publicationTags":885,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],"https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101039869","2018-2024",[889],"1689391c-5702-4349-aaa7-d720ee4321fc","NONE",{"id":892,"indexDatabase":893,"url":898,"indexYears":899,"academicFieldIds":900,"indexDatabaseRanking":18},"dadb15a8-ee22-41c2-a287-49e969d9a998",{"id":246,"createTime":18,"updateTime":18,"relativeEntities":894,"label":895,"description":896,"key":252,"publicationTags":897,"standard":18},[],{"EN":249,"VI":249},{"EN":251,"VI":251},[254],"https:\u002F\u002Fasean-cites.org\u002Fjournal_info?jid=10629","2016-2022",[901],"e04f14cf-280b-4aa8-b711-b77ddd79cbaf",{"id":903,"indexDatabase":904,"url":915,"indexYears":18,"academicFieldIds":916,"indexDatabaseRanking":18},"06f278ee-37b9-41eb-a9b0-3d2d77fa502b",{"id":905,"createTime":18,"updateTime":18,"relativeEntities":906,"label":907,"description":909,"key":912,"publicationTags":913,"standard":18},"88bab0f7-443b-476c-a72a-7fa5222da393",[],{"EN":908,"VI":908},"ISI\u002FESCI  - 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Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.",{"EN":979},"Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments",{"VOID":981},"Breslow NE, Day NE (1980) Statistical methods in cancer research, vol 1., The design and analysis of case–control studies IARC, Lyon\nBuzas JS (1998) Unbiased scores in proportional hazards regression with covariate measurement error. J Stat Plan Inference 67:247–257\nCarroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models: a modern perspective, 2nd edn. Chapman & Hall\u002FCRC, Boca Raton\nCox DR (1972) Regression models and life-tables (with Discussion). J R Stat Soc Ser B 34:187–220\nCox DR, Oakes D (1984) Analysis of survival data. Chapman & Hall\u002FCRC, Boca Raton\nFuchs HJ, Borowitz DS, Christiansen DH, Morris EM, Nash ML, Ramsey BW, Rosenstein BJ, Smith AL, Wohl ME (1994) Effect of aerosolized recombinant human DNase on exacerbations of respiratory symptoms and on pulmonary function in patients with cystic fibrosis. N Engl J Med 331:637–642\nHorn RA, Johnson CR (1985) Matrix analysis. Cambridge University Press, New York\nHu C, Lin DY (2004) Semiparametric failure time regression with replicates of mismeasured covariates. J Am Stat Assoc 99:105–118\nHu P, Tsiatis AA, Davidian M (1998) Estimating the parameters in the Cox model when covariates are measured with error. Biometrics 54:1407–1419\nHuang Y, Wang CY (2000) Cox regression with accurate covariates unascertainable: a nonparametric-correction approach. J Am Stat Assoc 45:1209–1219\nJiang J, Zhou H (2007) Additive hazard regression with auxiliary covariates. Biometrika 94:359–369\nKalbfleisch JD, Prentice RL (2002) The statistical analysis of failure time data, 2nd edn. Wiley, Hoboken\nKulich M, Lin DY (2000) Additive hazards regression with covariate measurement error. J Am Stat Assoc 95:238–248\nLi Y, Lin X (2003) Functional inference in frailty measurement error models for clustered survival data using the SIMEX approach. J Am Stat Assoc 98:191–203\nLi Y, Ryan L (2004) Survival analysis with heterogeneous covariate measurement error. J Am Stat Assoc 99:724–735\nLin DY, Ying Z (1994) Semiparametric analysis of the additive risk model. Biometrika 81:61–71\nNakamura T (1992) Proportional hazards model with covariates subject to measurement error. Biometrics 48:829–838\nPollard D (1990) Empirical processes: theory and applications. IMS, Hayward\nPrentice RL (1982) Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 69:331–342\nSong X, Huang Y (2005) On corrected score approach for proportional hazards model with covariate measurement error. Biometrics 61:702–714\nSun L, Zhang Z, Sun J (2006) Additive hazards regression of failure time data with covariate measurement errors. Stat Neerlandica 60:497–509\nvan der Vaart AW (1998) Asymptotic statistics. Cambridge University Press, New York\nWang CY, Hsu L, Feng ZD, Prentice RL (1997) Regression calibration in failure time regression. Biometrics 53:131–145\nYan Y, Yi GY (2015) A class of functional methods for error-contaminated survival data under additive hazards models with replicate measurements. J Am Stat Assoc. doi:10.1080\u002F01621459.2015.1034317\nYi GY, Lawless JF (2007) A corrected likelihood method for the proportional hazards model with covariates subject to measurement error. J Stat Plan Inference 137:1816–1828\nYi GY, Reid N (2010) A note on Mis-specified estimating functions. Stat Sinica 20:1749–1769\nZucker DM, Spiegelman D (2008) Corrected score estimation in the proportional hazards model with misclassified discrete covariates. Stat Med 27:1911–1933",{"VOID":983},"10.1007\u002Fs10985-015-9340-1","PUBLICATION","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10985-015-9340-1",[988,1004],{"id":989,"sortIndex":19,"researcher":18,"roles":990,"affiliations":992,"properties":1001,"displayName":1003,"givenName":18,"familyName":18},"a9ab1a64-6a9a-46eb-8ed6-f04e03f39f6f",[991],"AUTHOR",[993],{"id":994,"sortIndex":19,"affiliation":995,"properties":18},"ebe853b3-2b22-4df0-ac4a-affc6e3aa484",{"id":994,"createTime":18,"updateTime":18,"relativeEntities":996,"slug":18,"properties":997,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1000,"statistic":18},[],{"title":998},{"VI":999},"Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada",[],{"title":1002},{"VI":1003},"Ying Yan",{"id":1005,"sortIndex":200,"researcher":18,"roles":1006,"affiliations":1007,"properties":1014,"displayName":1016,"givenName":18,"familyName":18},"503e7bb0-300b-4534-a71d-8e7a21d89a69",[991],[1008],{"id":994,"sortIndex":19,"affiliation":1009,"properties":18},{"id":994,"createTime":18,"updateTime":18,"relativeEntities":1010,"slug":18,"properties":1011,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1013,"statistic":18},[],{"title":1012},{"VI":999},[],{"title":1015},{"VI":1016},"Grace Y. Yi","ARTICLE",{"url":986,"publisher":1019,"properties":1064},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1020,"slug":10,"properties":1021,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1024,"manageAffiliations":1033,"indexDatabases":1044,"url":18,"thumbnailPath":18,"statistic":1059,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1022,"title":1023},{"VOID":13},{"EN":15},[1025,1029],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1026,"label":1027,"description":1028,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1030,"label":1031,"description":1032,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1034,1039],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1035,"slug":18,"properties":1036,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1038,"statistic":18},[],{"title":1037},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1040,"slug":18,"properties":1041,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1043,"statistic":18},[],{"title":1042},{"EN":46},[48],[1045,1052],{"id":51,"indexDatabase":1046,"url":64,"indexYears":18,"academicFieldIds":1051,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1047,"label":1048,"description":1049,"key":60,"publicationTags":1050,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1053,"url":80,"indexYears":81,"academicFieldIds":1058,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1054,"label":1055,"description":1056,"key":77,"publicationTags":1057,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1060,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1061,"totalCitation":124,"totalCitationByYear":1062,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1063,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},{"pages":1065,"volume":1067},{"VOID":1066},"321-342",{"VOID":1068},"22","2015-09-02",2015,[85,62],false,{"id":1074,"createTime":1075,"updateTime":1076,"relativeEntities":1077,"slug":1078,"properties":1079,"entityType":984,"verifyStatus":188,"verifyTime":1076,"verifyNote":985,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1088,"fullTextUrl":18,"authors":1089,"publicationType":1017,"publisherRelationship":1155,"citationCount":18,"citationInfo":18,"publishDate":1206,"publishYear":1207,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1208,"openAccess":18,"references":18,"isForceReanalyzing":1072},"0084f499-ef4a-4df7-861c-ec48fe6f0e23","2024-01-24T11:28:51.960+00:00","2024-12-10T09:12:26.912+00:00",[],"Incorporating-delayed-entry-into-the-joint-frailty-model-for-recurrent-events-and-a-terminal-event",{"abstract":1080,"title":1082,"references":1084,"doi":1086},{"EN":1081},"In studies of recurrent events, joint modeling approaches are often needed to allow for potential dependent censoring by a terminal event such as death. Joint frailty models for recurrent events and death with an additional dependence parameter have been studied for cases in which individuals are observed from the start of the event processes. However, samples are often selected at a later time, which results in delayed entry so that only individuals who have not yet experienced the terminal event will be included. In joint frailty models such left truncation has effects on the frailty distribution that need to be accounted for in both the recurrence process and the terminal event process, if the two are associated. We demonstrate, in a comprehensive simulation study, the effects that not adjusting for late entry can have and derive the correctly adjusted marginal likelihood, which can be expressed as a ratio of two integrals over the frailty distribution. We extend the estimation method of Liu and Huang (Stat Med 27:2665–2683, 2008. \n                https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.3077\n                \n              ) to include potential left truncation. Numerical integration is performed by Gaussian quadrature, the baseline intensities are specified as piecewise constant functions, potential covariates are assumed to have multiplicative effects on the intensities. We apply the method to estimate age-specific intensities of recurrent urinary tract infections and mortality in an older population.",{"EN":1083},"Incorporating delayed entry into the joint frailty model for recurrent events and a terminal event",{"VOID":1085},"Abbring JH, van den Berg GJ (2007) The unobserved heterogeneity distribution in duration analysis. Biometrika 94(1):87–99\nBalan TA, Jonker MA, Johannesma PC, Putter H (2016) Ascertainment correction in frailty models for recurrent events data. Stat Med 35:4183–4201. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.6968\nCai Q, Wang M-C, Chan KCG (2017) Joint modeling of longitudinal, recurrent events and failure time data for survivor’s population. Biometrics 73:1150–1160. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fbiom.12693\nCaljouw MAA, van den Hout WB, Putter H, Achterberg WP, Cools HJM, Gussekloo J (2014) Effectiveness of cranberry capsules to prevent urinary tract infections in vulnerable older persons: a double-blind randomized placebo-controlled trial in long-term care facilities. J Am Geriatr Soc 62:103–110. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjgs.12593\nCook RJ, Lawless JF (1997) Marginal analysis of recurrent events and a terminating event. Stat Med 16:911–924\nCook RJ, Lawless JF (2007) The statistical analysis of recurrent events. Statistics for biology and health. Springer, New York\nCrowther MJ, Andersson TM-L, Lambert PC, Abrams KR, Humphreys K (2016) Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification. Stat Med 35:1193–1209. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.6779\nDuchateau L, Janssen P (2008) The frailty model. Springer, Berlin\nEmura T, Nakatochi M, Murotani K, Rondeau V (2017) A joint frailty-copula model between tumour progression and death for meta-analysis. Stat Methods Med Res 26:2649–2666. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0962280215604510\nEriksson F, Martinussen T, Scheike TH (2015) Clustered survival data with left-truncation. Scand J Stat 42:1149–1166. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fsjos.12157\nGhosh D, Lin DY (2003) Semiparametric analysis of recurrent events data in the presence of dependent censoring. Biometrics 59:877–885. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.0006-341X.2003.00102.x\nHuang C-Y, Wang M-C (2004) Joint modeling and estimation for recurrent event processes and failure time data. J Am Stat Assoc 99:1153–1165. https:\u002F\u002Fdoi.org\u002F10.1198\u002F016214504000001033\nJazić I, Haneuse S, French B, MacGrogan G, Rondeau V (2019) Design and analysis of nested case-control studies for recurrent events subject to a terminal event. Stat Med 38(22):4348–4362. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.8302\nJensen H, Brookmeyer R, Aaby P, Andersen PK (2004) Shared frailty model for left-truncated multivariate survival data. Museum Tusculanum, Biostatistisk Afdeling\nKalbfleisch JD, Schaubel DE, Ye Y, Gong Q (2013) An estimating function approach to the analysis of recurrent and terminal events. Biometrics 69:366–374. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fbiom.12025\nKlein JP, Moeschberger ML (2003) Survival analysis. Techniques for censored and truncated data, 2nd edn. Springer, Berlin\nLawless JF, Fong DYT (1999) State duration models in clinical and observational studies. Stat Med 18:2365–2376\nLiu D, Schaubel DE, Kalbfleisch JD (2012) Computationally efficient marginal models for clustered recurrent event data. Biometrics 68:637–647. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1541-0420.2011.01676.x\nLiu L, Huang X (2008) The use of Gaussian quadrature for estimation in frailty proportional hazards models. Stat Med 27:2665–2683. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.3077\nLiu L, Wolfe RA, Huang X (2004) Shared frailty models for recurrent events and a terminal event. Biometrics 60:747–756. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.0006-341X.2004.00225.x\nPiulachs X, Andrinopoulou E-R, Guillén M, Rizopoulos D (2021) A Bayesian joint model for zero-inflated integers and left-truncated event times with a time-varying association: applications to senior health care. Stat Med 40(1):147–166. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.8767\nR Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria\nRogers JK, Yaroshinsky A, Pocock SJ, Stokar D, Pogoda J (2016) Analysis of recurrent events with an associated informative dropout time: application of the joint frailty model. Stat Med 35:2195–2205. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.6853\nRondeau V, Mathoulin-Pelissier S, Jacqmin-Gadda H, Brouste V, Soubeyran P (2007) Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events. Biostatistics 8(4):708–721. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbiostatistics\u002Fkxl043\nRondeau V, Mauguen A, Laurent A, Berr C, Helmer C (2017) Dynamic prediction models for clustered and interval-censored outcomes: investigating the intra-couple correlation in the risk of dementia. Stat Methods Med Res 26(5):2168–2183\nRondeau V, Gonzalez JR, Mazroui Y, Mauguen A, Diakite A, Laurent A, Lopez M, Król A, Sofeu CL, Dumerc J, Rustand D, Chauvet J, Coent, Le Q (2021) frailtypack: general frailty models: shared, joint and nested frailty models with prediction; evaluation of failure-time surrogate endpoints. R package version 3.5.0. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=frailtypack\nvan den Berg GJ, Drepper B (2016) Inference for shared-frailty survival models with left-truncated data. Econ Rev 35:1075–1098. https:\u002F\u002Fdoi.org\u002F10.1080\u002F07474938.2014.975640\nvan den Hout A, Muniz-Terrera G (2016) Joint models for discrete longitudinal outcomes in aging research. J R Stat Soc Ser C 65:167–186. https:\u002F\u002Fdoi.org\u002F10.1111\u002Frssc.12114\nWienke A (2011) Frailty models in survival analysis. Chapman & Hall\u002FCRC Biostatistics Series, Boca Raton",{"VOID":1087},"10.1007\u002Fs10985-022-09587-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10985-022-09587-z",[1090,1114,1127,1142],{"id":1091,"sortIndex":19,"researcher":18,"roles":1092,"affiliations":1093,"properties":1111,"displayName":1113,"givenName":18,"familyName":18},"6d690a8d-8430-40c7-949b-107f999d5eb1",[991],[1094,1102],{"id":1095,"sortIndex":19,"affiliation":1096,"properties":18},"1bbffe8d-bcd5-45c8-8b04-8ed1bd74dcc5",{"id":1095,"createTime":18,"updateTime":18,"relativeEntities":1097,"slug":18,"properties":1098,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1101,"statistic":18},[],{"title":1099},{"VI":1100},"Max Planck Institute for Demographic Research, Rostock, Germany;",[],{"id":1103,"sortIndex":200,"affiliation":1104,"properties":1110},"64c489ac-2819-48b5-a35b-55e4c26e3e0d",{"id":1103,"createTime":18,"updateTime":18,"relativeEntities":1105,"slug":18,"properties":1106,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1109,"statistic":18},[],{"title":1107},{"VI":1108},"Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands",[],{},{"title":1112},{"VI":1113},"Marie Böhnstedt",{"id":1115,"sortIndex":200,"researcher":18,"roles":1116,"affiliations":1117,"properties":1124,"displayName":1126,"givenName":18,"familyName":18},"15ed827d-7d3e-4dd1-ab66-b7226bc2c6a6",[991],[1118],{"id":1095,"sortIndex":19,"affiliation":1119,"properties":18},{"id":1095,"createTime":18,"updateTime":18,"relativeEntities":1120,"slug":18,"properties":1121,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1123,"statistic":18},[],{"title":1122},{"VI":1100},[],{"title":1125},{"VI":1126},"Jutta Gampe",{"id":1128,"sortIndex":275,"researcher":18,"roles":1129,"affiliations":1130,"properties":1139,"displayName":1141,"givenName":18,"familyName":18},"bb2b4a45-df56-4e4a-8de9-3747806c8c4b",[991],[1131],{"id":1132,"sortIndex":19,"affiliation":1133,"properties":18},"ca9c3e8b-d720-4ebe-8340-22db1e2fe679",{"id":1132,"createTime":18,"updateTime":18,"relativeEntities":1134,"slug":18,"properties":1135,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1138,"statistic":18},[],{"title":1136},{"VI":1137},"Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands",[],{"title":1140},{"VI":1141},"Monique A. A. Caljouw",{"id":1143,"sortIndex":202,"researcher":18,"roles":1144,"affiliations":1145,"properties":1152,"displayName":1154,"givenName":18,"familyName":18},"8bb02f70-da32-4241-86c5-a921b4059c08",[991],[1146],{"id":1103,"sortIndex":19,"affiliation":1147,"properties":18},{"id":1103,"createTime":18,"updateTime":18,"relativeEntities":1148,"slug":18,"properties":1149,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1151,"statistic":18},[],{"title":1150},{"VI":1108},[],{"title":1153},{"VI":1154},"Hein Putter",{"url":1088,"publisher":1156,"properties":1201},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1157,"slug":10,"properties":1158,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1161,"manageAffiliations":1170,"indexDatabases":1181,"url":18,"thumbnailPath":18,"statistic":1196,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1159,"title":1160},{"VOID":13},{"EN":15},[1162,1166],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1163,"label":1164,"description":1165,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1167,"label":1168,"description":1169,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1171,1176],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1172,"slug":18,"properties":1173,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1175,"statistic":18},[],{"title":1174},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1177,"slug":18,"properties":1178,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1180,"statistic":18},[],{"title":1179},{"EN":46},[48],[1182,1189],{"id":51,"indexDatabase":1183,"url":64,"indexYears":18,"academicFieldIds":1188,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1184,"label":1185,"description":1186,"key":60,"publicationTags":1187,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1190,"url":80,"indexYears":81,"academicFieldIds":1195,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1191,"label":1192,"description":1193,"key":77,"publicationTags":1194,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1197,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1198,"totalCitation":124,"totalCitationByYear":1199,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1200,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},{"pages":1202,"volume":1204},{"VOID":1203},"585-607",{"VOID":1205},"29","2023-01-18",2023,[85,62],{"id":1210,"createTime":1211,"updateTime":1212,"relativeEntities":1213,"slug":1214,"properties":1215,"entityType":984,"verifyStatus":188,"verifyTime":1212,"verifyNote":985,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1226,"fullTextUrl":18,"authors":1227,"publicationType":1017,"publisherRelationship":1269,"citationCount":18,"citationInfo":18,"publishDate":1315,"publishYear":1316,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1317,"openAccess":18,"references":18,"isForceReanalyzing":1072},"015a1b44-6e28-406f-9b99-86e920f91be0","2024-04-06T18:40:29.721+00:00","2025-01-15T04:11:02.859+00:00",[],"Pseudo-value-regression-trees",{"abstract":1216,"title":1218,"keywords":1220,"references":1222,"doi":1224},{"EN":1217},"This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression framework, modeling individual-specific survival probabilities by computing pseudo-values and relating them to a set of covariates. The standard approach to pseudo-value regression is to fit a main-effects model using generalized estimating equations (GEE). PRT extend this approach by building a multivariate regression tree with pseudo-value outcome and by successively fitting a set of regularized additive models to the data in the nodes of the tree. Due to the combination of tree learning and additive modeling, PRT are able to perform variable selection and to identify relevant interactions between the covariates, thereby addressing several limitations of the standard GEE approach. In addition, PRT include time-dependent effects in the node-wise models. Interpretability of the PRT fits is ensured by controlling the tree depth. Based on the results of two simulation studies, we investigate the properties of the PRT method and compare it to several alternative modeling techniques. Furthermore, we illustrate PRT by analyzing survival in 3,652 patients enrolled for a randomized study on primary invasive breast cancer.",{"EN":1219},"Pseudo-value regression trees",{"EN":1221},"",{"VOID":1223},"Andersen PK, Pohar Perme M (2010) Pseudo-observations in survival analysis. Statist Methods Med Res 19:71–99\nAndersen PK, Klein JP, Rosthøj S (2003) Generalised linear models for correlated pseudo-observations, with applications to multi-state models. Biometrika 90:15–27\nBacchetti P, Segal MR (1995) Survival trees with time-dependent covariates: Application to estimating changes in the incubation period of AIDS. Lifetime Data Anal 1:35–47\nBinder N, Gerds TA, Andersen PK (2014) Pseudo-observations for competing risks with covariate dependent censoring. 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Ann Oncol 26(Suppl. 5):v8–v30\nStensrud MJ, Hernán MA (2020) Why test for proportional hazards? J Am Med Associat 323:1401–1402\nUno H, Cai T, Pencina MJ, D’Agostino RB, Wei LJ (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statist Med 30:1105–1117\nvan der Laan MJ, Robins JM (eds) (2003) Unified methods for censored longitudinal data and causality. Springer, New York\nvan der Ploeg T, Datema F, de Jong RB, Steyerberg EW (2014) Prediction of survival with alternative modeling techniques using pseudo values. PLoS One 9(6):e100234\nvon Minckwitz G, Untch M, Blohmer JU, Costa SD, Eidtmann H, Fasching PA, Gerber B, Eiermann W, Hilfrich J, Huober J, Jackisch C, Kaufmann M, Konecny GE, Denkert C, Nekljudova V, Mehta K, Loibl S (2012) Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 30:1796–1804\nVatcheva KP, Lee ML, McCormick JB, Rahbar MH (2015) The effect of ignoring statistical interactions in regression analyses conducted in epidemiologic studies: an example with survival analysis using Cox proportional hazards regression model. Epidemiology (Sunnyvale, Calif) 6(1):216\nZeileis A, Hornik K (2007) Generalized M-fluctuation tests for parameter instability. Statist Neerland 61:488–508\nZeileis A, Hothorn T, Hornik K (2008) Model-based recursive partitioning. J Computat Graph Statist 17:492–514\nZhao L, Feng D (2020) Deep neural networks for survival analysis using pseudo values. IEEE J Biomed Health Inform 24:3308–3314\nZhao L, Murray S, Mariani LH, Ju W (2020) Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: a pseudo-observation approach. Statist Med 39:3685–3699",{"VOID":1225},"10.1007\u002Fs10985-024-09618-x","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10985-024-09618-x",[1228,1243,1256],{"id":1229,"sortIndex":19,"researcher":18,"roles":1230,"affiliations":1231,"properties":1240,"displayName":1242,"givenName":18,"familyName":18},"c2138dc2-e34e-44d8-8ef4-85fccf534866",[991],[1232],{"id":1233,"sortIndex":19,"affiliation":1234,"properties":18},"c8220351-0b6b-49ef-b4b5-6ff5883a4bc1",{"id":1233,"createTime":18,"updateTime":18,"relativeEntities":1235,"slug":18,"properties":1236,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1239,"statistic":18},[],{"title":1237},{"VI":1238},"Institute of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany",[],{"title":1241},{"VI":1242},"Alina Schenk",{"id":1244,"sortIndex":200,"researcher":18,"roles":1245,"affiliations":1246,"properties":1253,"displayName":1255,"givenName":18,"familyName":18},"0dd15baf-e2aa-4aee-be0e-a84363d54eb3",[991],[1247],{"id":1233,"sortIndex":19,"affiliation":1248,"properties":18},{"id":1233,"createTime":18,"updateTime":18,"relativeEntities":1249,"slug":18,"properties":1250,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1252,"statistic":18},[],{"title":1251},{"VI":1238},[],{"title":1254},{"VI":1255},"Moritz Berger",{"id":1257,"sortIndex":275,"researcher":18,"roles":1258,"affiliations":1259,"properties":1266,"displayName":1268,"givenName":18,"familyName":18},"d4ea592d-0c34-4670-8acc-7a876deea960",[991],[1260],{"id":1233,"sortIndex":19,"affiliation":1261,"properties":18},{"id":1233,"createTime":18,"updateTime":18,"relativeEntities":1262,"slug":18,"properties":1263,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1265,"statistic":18},[],{"title":1264},{"VI":1238},[],{"title":1267},{"VI":1268},"Matthias Schmid",{"url":18,"publisher":1270,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1271,"slug":10,"properties":1272,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1275,"manageAffiliations":1284,"indexDatabases":1295,"url":18,"thumbnailPath":18,"statistic":1310,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1273,"title":1274},{"VOID":13},{"EN":15},[1276,1280],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1277,"label":1278,"description":1279,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1281,"label":1282,"description":1283,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1285,1290],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1286,"slug":18,"properties":1287,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1289,"statistic":18},[],{"title":1288},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1291,"slug":18,"properties":1292,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1294,"statistic":18},[],{"title":1293},{"EN":46},[48],[1296,1303],{"id":51,"indexDatabase":1297,"url":64,"indexYears":18,"academicFieldIds":1302,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1298,"label":1299,"description":1300,"key":60,"publicationTags":1301,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1304,"url":80,"indexYears":81,"academicFieldIds":1309,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1305,"label":1306,"description":1307,"key":77,"publicationTags":1308,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1311,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1312,"totalCitation":124,"totalCitationByYear":1313,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1314,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},"2024-02-25",2024,[85,62],{"id":1319,"createTime":1320,"updateTime":1320,"relativeEntities":1321,"slug":18,"properties":1322,"entityType":984,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1331,"fullTextUrl":18,"authors":1332,"publicationType":1017,"publisherRelationship":1363,"citationCount":18,"citationInfo":18,"publishDate":1414,"publishYear":1415,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1416,"openAccess":18,"references":18,"isForceReanalyzing":1072},"01fcea93-2631-4a52-b778-63306b41d8c8","2024-01-25T05:47:22.319+00:00",[],{"abstract":1323,"title":1325,"references":1327,"doi":1329},{"EN":1324},"Missing response problem is ubiquitous in survey sampling, medical, social science and epidemiology studies. It is well known that non-ignorable missing is the most difficult missing data problem where the missing of a response depends on its own value. In statistical literature, unlike the ignorable missing data problem, not many papers on non-ignorable missing data are available except for the full parametric model based approach. In this paper we study a semiparametric model for non-ignorable missing data in which the missing probability is known up to some parameters, but the underlying distributions are not specified. By employing Owen (1988)’s empirical likelihood method we can obtain the constrained maximum empirical likelihood estimators of the parameters in the missing probability and the mean response which are shown to be asymptotically normal. Moreover the likelihood ratio statistic can be used to test whether the missing of the responses is non-ignorable or completely at random. The theoretical results are confirmed by a simulation study. As an illustration, the analysis of a real AIDS trial data shows that the missing of CD4 counts around two years are non-ignorable and the sample mean based on observed data only is biased.",{"EN":1326},"Empirical likelihood method for non-ignorable missing data problems",{"VOID":1328},"Alho JM (1990) Adjusting for nonresponse bias using logistic regression. Biometrika 77:617–624\nChan KCG, Yam SCP (2014) Oracle, multiple robust and multipurpose calibration in a missing response problem. Stat Sci 29:380–396\nChen J, Qin J (1993) Empirical likelihood estimation for finite populations and the effective usage of auxiliary information. Biometrika 80:107–116\nChen K (2001) Parametric models for response-biased sampling. J R Stat Soc Ser B Stat Methodol 63:775–789\nCochran WG (1977) Sampling techniques, 3rd edn., Wiley series in probability and mathematical statisticsWiley, New York\nDavidian M, Tsiatis AA, Leon S (2005) Semiparametric estimation of treatment effect in a pretest-posttest study with missing data. Stat Sci 20:261–301 with comments and a rejoinder by the authors\nGodambe VP (1960) An optimum property of regular maximum likelihood estimation. Ann Math Stat 31:1208–1211\nGreenlees JS, Reece WS, Zieschan KD (1982) Imputation of missing values when the probability of response depends on the variable being imputed. J Am Stat Assoc 77:251–261\nHall P, Scala BL (1990) Methodology and algorithms of empirical likelihood. Int Stat Rev\u002FRevue Internationale de Statistique 58:109–127\nHammer SM, Katzenstein DA, Hughes MD, Gundacker H, Schooley RT, Haubrich RH, Henry WK, Lederman MM, Phair JP, Niu M, Hirsch MS, Merigan TC (1996) A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. N Engl J Med 335:1081–1090\nHan P, Wang L (2013) Estimation with missing data: beyond double robustness. Biometrika 100:417–430\nKim JK, Im J (2014) Propensity score adjustment with several follow-ups. Biometrika 101:439–448\nKim JK, Yu CL (2011) A semi-parametric estimation of mean functionals with non-ignorable missing data. J Am Stat Assoc 106:157–165\nLi L, Shen C, Li X, Robins JM (2011) On weighting approaches for missing data. Stat Methods Med Res\nLiang K-Y, Qin J (2000) Regression analysis under non-standard situations: a pairwise pseudolikelihood approach. J R Stat Soc Ser B 62:773–786\nLittle RJA (1982) Models for nonresponse in sample surveys. J Am Stat Assoc 77:237–250\nLittle RJA, Rubin RB (2002) Statistical analysis with missing data, 2nd edn., Wiley series in probability and statisticsWiley, Hoboken\nNevo A (2003) Using weights to adjust for sample selection when auxiliary information is available. J Bus Econ Stat 21:43–52\nNiu C, Guo X, Xu W, Zhu L (2014) Empirical likelihood inference in linear regression with nonignorable missing response. Comput Stat Data Anal 79:91–112\nOwen AB (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75:237–249\nOwen AB (2001) Empirical likelihood. Chapman & Hall, Boca Raton\nQin J, Zhang B (2007) Empirical-likelihood-based inference in missing response problems and its application in observational studies. J R Stat Soc Ser B 69:101–122\nRotnitzky A, Robins JM (1997) Analysis of semi-parametric regression models with non-ignorable non-response. Stat Med 16:81–102\nScharfstein DO, Rotnitzky A, Robins JM (1999) Adjusting for nonignorable drop-out using semiparametric nonresponse models. J Am Stat Assoc 94:1096–1146 with comments and a rejoinder by the authors\nSmall CG, McLeish DL (1988) Generalizations of ancillarity, completeness and sufficiency in an inference function space. Ann Stat 16:534–551\nSmall CG, McLeish DL (1989) Projection as a method for increasing sensitivity and eliminating nuisance parameters. Biometrika 76:693–703\nTan Z (2010) Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika 97:661–682\nTang CY, Leng C (2011) Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika 98:1001–1006\nTang CY, Qin Y (2012) An efficient empirical likelihood approach for estimating equations with missing data. Biometrika 99:1001–1007\nTang G, Little RJA, Raghunathan TE (2003) Analysis of multivariate missing data with nonignorable nonresponse. Biometrika 90:747–764\nVardi Y (1982) Nonparametric estimation in the presence of length bias. Ann Stat 10:616–620\nVardi Y (1985) Empirical distributions in selection bias models. Ann Stat 13:178–205 with discussion by C. L. Mallows\nWang Q, Dai P (2008) Semiparametric model-based inference in the presence of missing responses. Biometrika 95:721–734\nWang S, Shao J, Kim JK (2014) An instrument variable approach for identification and estimation with nonignorable nonresponse. Stat Sin 24:1097–1116\nZhao P-Y, Tang M-L, Tang N-S (2013) Robust estimation of distribution functions and quantiles with non-ignorable missing data. Can J Stat 41:575–595\nZhong P-S, Chen S (2014) Jackknife empirical likelihood inference with regression imputation and survey data. J Multivar Anal 129:193–205\nZhou Y, Wan ATK, Wang X (2008) Estimating equations inference with missing data. J Am Stat Assoc 103:1187–1199",{"VOID":1330},"10.1007\u002Fs10985-016-9381-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10985-016-9381-0",[1333,1348],{"id":1334,"sortIndex":19,"researcher":18,"roles":1335,"affiliations":1336,"properties":1345,"displayName":1347,"givenName":18,"familyName":18},"d587d602-ba55-4786-b31d-539943ebe140",[991],[1337],{"id":1338,"sortIndex":19,"affiliation":1339,"properties":18},"4ca16f79-2583-4f02-a407-209096af8679",{"id":1338,"createTime":18,"updateTime":18,"relativeEntities":1340,"slug":18,"properties":1341,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1344,"statistic":18},[],{"title":1342},{"VI":1343},"Department of Mathematical Sciences, Indiana University South Bend, South Bend, USA",[],{"title":1346},{"VI":1347},"Zhong Guan",{"id":1349,"sortIndex":200,"researcher":18,"roles":1350,"affiliations":1351,"properties":1360,"displayName":1362,"givenName":18,"familyName":18},"02eaa458-5de5-4cc8-b86e-074ea2a9fc1e",[991],[1352],{"id":1353,"sortIndex":19,"affiliation":1354,"properties":18},"b5f440ce-1231-4408-90b4-7d7d855a8967",{"id":1353,"createTime":18,"updateTime":18,"relativeEntities":1355,"slug":18,"properties":1356,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1359,"statistic":18},[],{"title":1357},{"VI":1358},"Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, USA",[],{"title":1361},{"VI":1362},"Jing Qin",{"url":1331,"publisher":1364,"properties":1409},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1365,"slug":10,"properties":1366,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1369,"manageAffiliations":1378,"indexDatabases":1389,"url":18,"thumbnailPath":18,"statistic":1404,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1367,"title":1368},{"VOID":13},{"EN":15},[1370,1374],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1371,"label":1372,"description":1373,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1375,"label":1376,"description":1377,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1379,1384],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1380,"slug":18,"properties":1381,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1383,"statistic":18},[],{"title":1382},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1385,"slug":18,"properties":1386,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1388,"statistic":18},[],{"title":1387},{"EN":46},[48],[1390,1397],{"id":51,"indexDatabase":1391,"url":64,"indexYears":18,"academicFieldIds":1396,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1392,"label":1393,"description":1394,"key":60,"publicationTags":1395,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1398,"url":80,"indexYears":81,"academicFieldIds":1403,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1399,"label":1400,"description":1401,"key":77,"publicationTags":1402,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1405,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1406,"totalCitation":124,"totalCitationByYear":1407,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1408,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},{"pages":1410,"volume":1412},{"VOID":1411},"113-135",{"VOID":1413},"23","2016-09-19",2016,[85,62],{"id":1418,"createTime":1419,"updateTime":1420,"relativeEntities":1421,"slug":1422,"properties":1423,"entityType":984,"verifyStatus":188,"verifyTime":1420,"verifyNote":985,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1432,"fullTextUrl":18,"authors":1433,"publicationType":1017,"publisherRelationship":1488,"citationCount":18,"citationInfo":18,"publishDate":1539,"publishYear":1540,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1541,"openAccess":18,"references":18,"isForceReanalyzing":1072},"02706d23-6325-4970-85b4-6856f52ca01a","2023-12-05T13:31:31.531+00:00","2024-12-09T15:57:32.812+00:00",[],"Group-and-within-group-variable-selection-for-competing-risks-data",{"abstract":1424,"title":1426,"references":1428,"doi":1430},{"EN":1425},"Variable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic properties, including variable selection consistency at group and within-group levels. We also show superior performance in simulated and real data sets over several competing approaches, including group bridge, adaptive group lasso, and AIC \u002F BIC-based methods.",{"EN":1427},"Group and within-group variable selection for competing risks data",{"VOID":1429},"Cai J, Fan J, Li R, Zhou H (2005) Variable selection for multivariate failure time data. Biometrika 92:303–316\nCommenges D, Andersen PK (1995) Score test of homogeneity for survival data. Lifetime Data Anal 1:145–156\nFan J, Li R (2002) Variable selection for Cox’s proportional hazards model and frailty properties. J Am Stat Assoc 30:74–99\nFine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509\nFu Z (2015) crrp: Penalized variable selection in competing risks regression. http:\u002F\u002FCRAN.R-project.org\u002Fpackage=crrp, r package version 1.0\nFu Z, Parikh CR, Zhou B (2016a) Penalized variable selection in competing risks regression. Lifetime Data Anal. doi:10.1007\u002Fs10985-016-9362-3\nFu Z, Ma S, Lin H, Parikh CR, Zhou B (2016b) Penalized variable selection for multi-center competing risks data. Stat Biosci. doi:10.1007\u002Fs12561-016-9181-9\nGray RJ (1988) A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat 16:1141–1154\nHa ID, Lee M, Oh S, Jeong JH, Sylvester R, Lee Y (2014) Variable selection in subdistribution hazard frailty models with competing risks data. Stat Med 33:4590–604\nHuang J, Ma S, Xie H, Zhang CH (2009) A group bridge approach for variable selection. Biometrika 96:339–355\nHuang J, Li L, Liu Y, Zhao X (2014) Group selection in the Cox model with a diverging number of covariates. Stat Sin 24:1787–1810\nKim HT, Zhang MJ, Woolfrey AE, Martin AS, Chen J, Saber W, Perales MA, Armand P, Eapen M (2016) Donor and recipient sex in allogeneic stem cell transplantation: what really matters. Haematologica 101:1260–1266\nKroger N, Solano C, Wolschke C et al (2016) Antilymphocyte globulin for prevention of chronic graft-versus-host disease. New Engl J Med 374:43–53\nKuk D, Varadhan R (2013) Model selection in competing risks regression. Stat Med 32:3077–3088\nLogan B, Zhang MJ, Klein JP (2011) Marginal models for clustered time to event data with competing risks using pseudovalues. Biometrics 67:1–7\nPrentice RL, Kalbfleisch JD, Peterson AV, Flournoy N, Farewell VT, Breslow NE (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554\nRubio MT, Labopin M, Blaise D et al (2015) The impact of graft-versus-host disease prophylaxis in reduced-intensity conditioning allogeneic stem cell transplant in acute myeloid leukemia: a study from the acute leukemia working party of the European group for blood and marrow transplantation. Haematologica 100:683–689\nSeetharaman I (2013) Consistent bi-level variable selection via composite group bridge penalized regression. Master’s thesis, Kansas State University, KS, USA\nShaw PJ, Kan F, Ahn KW, Spellman SR, Aljurf M, Ayas M et al (2010) Outcomes of pediatric bone marrow transplantation for leukemia and myelodysplasia using matched sibling, mismatched related, or matched unrelated donors. Blood 116:4007–4015\nVaradhan R, Kuk D (2015) crrstep: Stepwise covariate selection for the Fine and Gray competing risks regression model. http:\u002F\u002FCRAN.R-project.org\u002Fpackage=crrstep, r package version 2015-2.1\nWang HJ, Zhou J, Li Y (2013) Variable selection for censored quantile regression. Stat Sin 23:145–167\nWu TT, Wang S (2013) Doubly regularized Cox regression for high-dimensional survival data with group structures. Stat Interface 6:175–186\nYuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B 68:49–67\nZhou B, Fine J, Latouche A, Labopin M (2012) Competing risks regression for clustered data. Biostatistics 13:371–383\nZou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101:1418–1429",{"VOID":1431},"10.1007\u002Fs10985-017-9400-9","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10985-017-9400-9",[1434,1449,1462,1475],{"id":1435,"sortIndex":19,"researcher":18,"roles":1436,"affiliations":1437,"properties":1446,"displayName":1448,"givenName":18,"familyName":18},"4afb6305-50a9-4ce9-928e-a7b70a5d4a91",[991],[1438],{"id":1439,"sortIndex":19,"affiliation":1440,"properties":18},"4520b820-871c-4ffb-84c3-1fec4a50726e",{"id":1439,"createTime":18,"updateTime":18,"relativeEntities":1441,"slug":18,"properties":1442,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1445,"statistic":18},[],{"title":1443},{"VI":1444},"Division of Biostatistics, Medical College of Wisconsin, Milwaukee, USA",[],{"title":1447},{"VI":1448},"Kwang Woo 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paper examines modeling and inference questions for experiments in which different subsets of a set of k possibly dependent components are tested in r different environments. In each environment, the failure times of the set of components on test is assumed to be governed by a particular type of multivariate exponential (MVE) distribution. For any given component tested in several environments, it is assumed that its marginal failure rate varies from one environment to another via a change of scale between the environments, resulting in a joint MVE model which links in a natural way the applicable MVE distributions describing component behavior in each fixed environment. This study thus extends the work of Proschan and Sullo (1976) to multiple environments and the work of Kvam and Samaniego (1993) to dependent data. The problem of estimating model parameters via the method of maximum likelihood is examined in detail. First, necessary and sufficient conditions for the identifiability of model parameters are established. We then treat the derivation of the MLE via a numerically-augmented application of the EM algorithm. The feasibility of the estimation method is demonstrated in an example in which the likelihood ratio test of the hypothesis of equal component failure rates within any given environment is carried out.",{"EN":1550},"Multivariate Life Testing in Variably Scaled Environments",{"VOID":1552},"D. W. Cowan, H. J. Thompson, H. J. Paulus, and P. W. Mielke, “Bronchial asthma associated with air pollutants from the grain industry,” Journal of the Air Pollution Control Association vol. 13, pp. 546–552, 1963.\nA. P. Dempster, A. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM Algorithm,” Journal of the Royal Statistical Society, Ser. B vol. 39, pp. 1–38, 1977.\nP. H. Kvam and F. J. Samaniego, “Life testing in variably scaled environments,” Technometrics vol. 35, pp. 306–314, 1993.\nP. H. Kvam and F. J. Samaniego, “Multivariate Life Testing in Variably Scaled Environments.” Technical Report, Division of Statistics, University of California, Davis, 1997.\nD. V. Lindley and N. D. Singpurwalla, “Multivariate distributions for the life lengths of components of a system sharing a common environment,” Journal of Applied Probability vol. 23, pp. 418–431, 1986.\nT. Makelainen, K. Schmidt, and G. P. H. Styan, “On the existence and uniqueness of the maximum likelihood estimate of a vector-valued parameter in fixed-sample sizes,” The Annals of Statistics vol. 9, pp. 758–767, 1981.\nA. W. Marshall and I. Olkin, “A multivariate exponential distribution,” Journal of the American Statistical Association vol. 62, pp. 32–44, 1967.\nP. W. Mielke and M. M. Siddiqui, “A combinatorial test for independence of dichotomous responses,” Journal of the American Statistical Association vol. 60, pp. 437–441, 1965.\nW. Nelson, Accelerated Testing. John Wiley: New York, 1990.\nJ. M. Ortega and C. R. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables. Academic Press: New York, 1970.\nF. Proschan and P. Sullo, “Estimating the parameters of a multivariate exponential distribution,” Journal of the American Statistical Association vol. 71, pp. 465–472, 1976.\nM. Zelen, “Factorial experiments in life testing,” Technometrics vol. 1, pp. 269–288, 1959.",{"VOID":1554},"10.1023\u002FA:1009602128877","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1009602128877",[1557,1572],{"id":1558,"sortIndex":19,"researcher":18,"roles":1559,"affiliations":1560,"properties":1569,"displayName":1571,"givenName":18,"familyName":18},"a35e1e55-3709-4792-b050-101be6d798b2",[991],[1561],{"id":1562,"sortIndex":19,"affiliation":1563,"properties":18},"781e36bf-2ad1-4e5b-9cca-b4f52b2de76f",{"id":1562,"createTime":18,"updateTime":18,"relativeEntities":1564,"slug":18,"properties":1565,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1568,"statistic":18},[],{"title":1566},{"VI":1567},"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta",[],{"title":1570},{"VI":1571},"P. H. Kvam",{"id":1573,"sortIndex":200,"researcher":18,"roles":1574,"affiliations":1575,"properties":1584,"displayName":1586,"givenName":18,"familyName":18},"82b14daa-4797-43ed-bdf6-34e7aa9ed662",[991],[1576],{"id":1577,"sortIndex":19,"affiliation":1578,"properties":18},"208bf2a1-c691-422d-9104-43cde14c6808",{"id":1577,"createTime":18,"updateTime":18,"relativeEntities":1579,"slug":18,"properties":1580,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1583,"statistic":18},[],{"title":1581},{"VI":1582},"Division of Statistics, University of California, Davis, Davis",[],{"title":1585},{"VI":1586},"F. J. Samaniego",{"url":1555,"publisher":1588,"properties":1633},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1589,"slug":10,"properties":1590,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1593,"manageAffiliations":1602,"indexDatabases":1613,"url":18,"thumbnailPath":18,"statistic":1628,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1591,"title":1592},{"VOID":13},{"EN":15},[1594,1598],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1595,"label":1596,"description":1597,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1599,"label":1600,"description":1601,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1603,1608],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1604,"slug":18,"properties":1605,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1607,"statistic":18},[],{"title":1606},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1609,"slug":18,"properties":1610,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1612,"statistic":18},[],{"title":1611},{"EN":46},[48],[1614,1621],{"id":51,"indexDatabase":1615,"url":64,"indexYears":18,"academicFieldIds":1620,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1616,"label":1617,"description":1618,"key":60,"publicationTags":1619,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1622,"url":80,"indexYears":81,"academicFieldIds":1627,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1623,"label":1624,"description":1625,"key":77,"publicationTags":1626,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1629,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1630,"totalCitation":124,"totalCitationByYear":1631,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1632,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},{"pages":1634,"volume":1636},{"VOID":1635},"337-351",{"VOID":1637},"3","1997-12-01",1997,[85,62],{"id":1642,"createTime":1643,"updateTime":1644,"relativeEntities":1645,"slug":1646,"properties":1647,"entityType":984,"verifyStatus":188,"verifyTime":1644,"verifyNote":985,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1656,"fullTextUrl":18,"authors":1657,"publicationType":1017,"publisherRelationship":1686,"citationCount":18,"citationInfo":18,"publishDate":1736,"publishYear":1207,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1737,"openAccess":18,"references":18,"isForceReanalyzing":1072},"03045bc9-d3ce-4aea-8def-0517bca8634f","2023-12-11T14:55:07.849+00:00","2024-12-23T08:43:00.957+00:00",[],"Volume-under-the-ROC-surface-for-high-dimensional-independent-screening-with-ordinal-competing-risk-outcomes",{"abstract":1648,"title":1650,"references":1652,"doi":1654},{"EN":1649},"We propose a screening method for high-dimensional data with ordinal competing risk outcomes, which is time-dependent and model-free. Existing methods are designed for cause-specific variable screening and fail to evaluate how a biomarker is associated with multiple competing events simultaneously. The proposed method utilizes the Volume under the ROC surface (VUS), which measures the concordance between values of a biomarker and event status at certain time points and provides an overall evaluation of the discrimination capacity of a biomarker. We show that the VUS possesses the sure screening property, i.e., true important covariates can be retained with probability tending to one, and the size of the selected set can be bounded with high probability. The VUS appears to be a viable model-free screening metric as compared to some existing methods in simulation studies, and it is especially robust to data contamination. Through an analysis of breast-cancer gene-expression data, we illustrate the unique insights into the overall discriminatory capability provided by the VUS.",{"EN":1651},"Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes",{"VOID":1653},"Budczies J, Kosztyla D (2021) cancerdata: development and validation of diagnostic tests from high-dimensional molecular data: datasets. R package version 1.30.0\nChen X, Li C, Zhang T, Gao Z (2022) On correlation rank screening for ultra-high dimensional competing risks data. J Appl Stat 49(7):1848–1864. https:\u002F\u002Fdoi.org\u002F10.1080\u002F02664763.2021.1884209\nFan J, Lv J (2008) Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Ser B (Stat Methodol) 70(5):849–911\nFan J, Feng Y, Wu Y (2010) High-dimensional variable selection for Cox’s proportional hazards model, Collections, vol 6. Institute of Mathematical Statistics, Beachwood, pp 70–86\nFine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509\nGerds TA, Scheike TH, Andersen PK (2012) Absolute risk regression for competing risks: interpretation, link functions, and prediction. Stat Med 31(29):3921–3930\nGorst-Rasmussen A, Scheike T (2013) Independent screening for single-index hazard rate models with ultrahigh dimensional features. J R Stat Soc Ser B (Stat Methodol) 75(2):217–245\nHong HG, Chen X, Christiani DC, Li Y (2018) Integrated powered density: screening ultrahigh dimensional covariates with survival outcomes. Biometrics 74(2):421–429\nLi J, Fine JP (2008) ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies. Biostatistics 9(3):566–576\nLi J, Zheng Q, Peng L, Huang Z (2016) Survival impact index and ultrahigh-dimensional model-free screening with survival outcomes. Biometrics 72(4):1145–1154\nLi E, Mei B, Tian M (2018) Feature screening based on ultrahigh dimensional competing risks models. Sci Sin Math 48(8):1061. https:\u002F\u002Fdoi.org\u002F10.1360\u002FN012017-00069\nLiu Y, Chen X, Wang H (2021) The fused Kolmogorov–Smirnov screening for ultra-high dimensional semi-competing risks data. Appl Math Model 98:109–120. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.apm.2021.04.031\nLu S, Chen X, Xu S, Liu C (2020) Joint model-free feature screening for ultra-high dimensional semi-competing risks data. Comput Stat Data Anal 147:106942\nMossman D (1999) Three-way rocs. Med Decis Mak 19(1):78–89\nNakas CT, Yiannoutsos CT (2004) Ordered multiple-class ROC analysis with continuous measurements. Stat Med 23(22):3437–3449\nPan J, Yu Y, Zhou Y (2018) Nonparametric independence feature screening for ultrahigh-dimensional survival data. Metrika 81(7):821–847\nPeng M, Xiang L (2021) Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data. Stat Methods Med Res 30(11):2428–2446. https:\u002F\u002Fdoi.org\u002F10.1177\u002F09622802211037071\nSong R, Lu W, Ma S, Jessie Jeng X (2014) Censored rank independence screening for high-dimensional survival data. Biometrika 101(4):799–814\nTian B, Liu Z, Wang H (2022) Non-marginal feature screening for varying coefficient competing risks model. Stat Probab Lett 190:109648. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.spl.2022.109648\nTibshirani RJ (2009) Univariate shrinkage in the cox model for high dimensional data. Stat Appl Genet Mol Biol 8(1):21\nvan de Vijver MJ, He YD, Van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. New Engl J Med 347(25):1999–2009\nWang H, Shen Z, Tan Z, Zhang Z, Li G (2022) Fast lasso-type safe screening for fine-gray competing risks model with ultrahigh dimensional covariates. Stat Med 41(24):4941–4960. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.9545\nZhang S, Qu Y, Cheng Y, Lopez OL, Wahed AS (2022) Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces. Lifetime Data Anal 28(1):1–22. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10985-021-09539-z\nZhao SD, Li Y (2012) Principled sure independence screening for cox models with ultra-high-dimensional covariates. J Multivar Anal 105(1):397–411. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmva.2011.08.002",{"VOID":1655},"10.1007\u002Fs10985-023-09600-z","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10985-023-09600-z",[1658,1673],{"id":1659,"sortIndex":19,"researcher":18,"roles":1660,"affiliations":1661,"properties":1670,"displayName":1672,"givenName":18,"familyName":18},"ce55cec9-94ac-40e9-be76-f248ad6b2de9",[991],[1662],{"id":1663,"sortIndex":19,"affiliation":1664,"properties":18},"9ddf8ef0-d2b3-4185-90cf-eedc2c64c0f3",{"id":1663,"createTime":18,"updateTime":18,"relativeEntities":1665,"slug":18,"properties":1666,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1669,"statistic":18},[],{"title":1667},{"VI":1668},"Department of Statistics, University of Pittsburgh, Pittsburgh, USA",[],{"title":1671},{"VI":1672},"Yang 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the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.","Trong tài liệu nghiên cứu dữ liệu sự kiện tái phát, một lượng lớn công trình đã tập trung vào các quá trình sự kiện tái phát đơn biến, trong đó sự xuất hiện của mỗi sự kiện được coi là một điểm thời gian đơn lẻ. Tuy nhiên, có nhiều ứng dụng mà sự kiện tái phát đơn biến không đủ khả năng để mô tả đặc điểm của quá trình vì bệnh nhân trải qua những khoảng thời gian không t trivial liên quan đến mỗi sự kiện. Điều này dẫn đến một quá trình sự kiện luân phiên, trong đó tình trạng bệnh tật của bệnh nhân thay đổi giữa các giai đoạn tiến triển và thuyên giảm. Trong bài báo này, chúng tôi xem xét động lực của một bệnh mãn tính và quá trình thuyên giảm - tiến triển liên quan của nó trên hai thang thời gian: thời gian lịch và thời gian kể từ khi khởi phát. Cụ thể, trên thời gian lịch, chúng tôi khám phá động lực dân số và mối quan hệ giữa tỷ lệ mắc bệnh, tỷ lệ tồn tại và khoảng thời gian cho các quá trình sự kiện luân phiên như vậy. Chúng tôi cung cấp các kỹ thuật ước lượng phi tham số cho các đại lượng đặc trưng của quá trình. Trong một số bối cảnh, các quá trình tiến triển được quan sát từ một thời điểm khởi phát cho đến khi tử vong; để xem xét mối quan hệ giữa quá trình sống sót và các quá trình sự kiện luân phiên, các phương pháp phi tham số được phát triển để ước lượng quá trình tiến triển trong suốt thời gian sống. Bằng cách hiểu động lực dân số và cấu trúc bên trong quá trình, bài báo cung cấp một cách mới và tổng quát để nghiên cứu các quá trình sự kiện luân phiên.",{"EN":1749,"VI":1750},"Alternating event processes during lifetimes: population dynamics and statistical inference","Quá trình sự kiện luân phiên trong suốt cuộc sống: động lực dân số và suy diễn thống kê",{"EN":1221,"VI":1221},{"VOID":1753},"Andersen PK, Gill RD (1982) Cox’s regression model for counting processes: a large sample study. Ann Stat 10(4):1100–1120\nCook RJ, Lawless JF (2007) The statistical analysis of recurrent events. Springer, Berlin\nFleming TR, Harrington DP (1991) Counting processes and survival analysis, vol 8. Wiley, New York\nGogtay N, Vyas NS, Testa R, Wood SJ, Pantelis C (2011) Age of onset of schizophrenia: perspectives from structural neuroimaging studies. Schizophr Bull 37(3):504–513\nHu XJ, Lagakos SW (2007) Nonparametric estimation of the mean function of a stochastic process with missing observations. Lifetime Data Anal 13(1):51–73\nHu XJ, Lorenzi M, Spinelli JJ, Ying SC, McBride ML (2011) Analysis of recurrent events with non-negligible event duration, with application to assessing hospital utilization. Lifetime Data Anal 17(2):215–233\nHuang CY, Wang MC (2004) Joint modeling event and estimation for recurrent processes and failure time data. J Am Stat Assoc 99(468):1153–1165\nLin DY, Wei LJ, Yang I, Ying Z (2000) Semiparametric regression for the mean and rate functions of recurrent events. J R Stat Soc B 62(4):711–730\nMüller H-G (1991) Smooth optimum kernel estimators near endpoints. Biometrika 78(3):521–530\nMunk-Jorgensen P, Mortensen PB (1992) Incidence and other aspects of the epidemiology of schizophrenia in denmark, 1971–87. Br J Psychiatry 161(4):489\nPena EA, Strawderman RL, Hollander M (2001) Nonparametric estimation with recurrent event data. J Am Stat Assoc 96(456):1299–1315\nPrentice RL, Williams BJ, Peterson AV (1981) On the regression analysis of multivariate failure time data. Biometrika 68(2):373\nRoss SM (1983) Stochastic processes, vol 23. Wiley, New York\nSchaubel DE, Cai J (2005) Semiparametric methods for clustered recurrent event data. Lifetime Data Anal 11(3):405–425\nWang MC (2005) Length bias. In: Armitage P, Colton T (eds) Encyclopedia of biostatistics. Wiley, New York, pp 2756–9\nWang MC, Chang SH (1999) Nonparametric estimation of a recurrent survival function. J Am Stat Assoc 94(445):146–153\nWang MC, Qin J, Chiang CT (2001) Analyzing recurrent event data with informative censoring. J Am Stat Assoc 96:1057–1065\nYan J, Fine JP (2008) Analysis of episodic data with application to recurrent pulmonary exacerbations in cystic fibrosis patients. J Am Stat Assoc 103(482):498–510\nYe Y, Kalbfleisch JD, Schaubel DE (2007) Semiparametric analysis of correlated recurrent and terminal events. Biometrics 63(1):78–87",{"VOID":1755},"10.1007\u002Fs10985-017-9404-5","2025-02-05T17:39:32.272+00:00",[191],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10985-017-9404-5",[1760,1775,1790],{"id":1761,"sortIndex":19,"researcher":18,"roles":1762,"affiliations":1763,"properties":1772,"displayName":1774,"givenName":18,"familyName":18},"8e821ddc-df03-4ff7-b0a2-ad7de0ead0c3",[991],[1764],{"id":1765,"sortIndex":19,"affiliation":1766,"properties":18},"26219fe4-c511-4070-b979-276d7c8b735b",{"id":1765,"createTime":18,"updateTime":18,"relativeEntities":1767,"slug":18,"properties":1768,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1771,"statistic":18},[],{"title":1769},{"VI":1770},"Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA",[],{"title":1773},{"VI":1774},"Russell T. Shinohara",{"id":1776,"sortIndex":200,"researcher":18,"roles":1777,"affiliations":1778,"properties":1787,"displayName":1789,"givenName":18,"familyName":18},"89f4167c-a7c6-4e1b-8109-11938b36dff9",[991],[1779],{"id":1780,"sortIndex":19,"affiliation":1781,"properties":18},"86587da8-a6c8-442c-9318-8775af862655",{"id":1780,"createTime":18,"updateTime":18,"relativeEntities":1782,"slug":18,"properties":1783,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1786,"statistic":18},[],{"title":1784},{"VI":1785},"Department of Biostatistics, Johns Hopkins University, Baltimore, USA",[],{"title":1788},{"VI":1789},"Yifei Sun",{"id":1791,"sortIndex":275,"researcher":18,"roles":1792,"affiliations":1793,"properties":1800,"displayName":1802,"givenName":18,"familyName":18},"84bec487-8f69-4fd9-a6cc-c14ba4b6c972",[991],[1794],{"id":1780,"sortIndex":19,"affiliation":1795,"properties":18},{"id":1780,"createTime":18,"updateTime":18,"relativeEntities":1796,"slug":18,"properties":1797,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1799,"statistic":18},[],{"title":1798},{"VI":1785},[],{"title":1801},{"VI":1802},"Mei-Cheng Wang",{"url":18,"publisher":1804,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1805,"slug":10,"properties":1806,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1809,"manageAffiliations":1818,"indexDatabases":1829,"url":18,"thumbnailPath":18,"statistic":1844,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1807,"title":1808},{"VOID":13},{"EN":15},[1810,1814],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1811,"label":1812,"description":1813,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1815,"label":1816,"description":1817,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1819,1824],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1820,"slug":18,"properties":1821,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1823,"statistic":18},[],{"title":1822},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1825,"slug":18,"properties":1826,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1828,"statistic":18},[],{"title":1827},{"EN":46},[48],[1830,1837],{"id":51,"indexDatabase":1831,"url":64,"indexYears":18,"academicFieldIds":1836,"indexDatabaseRanking":18},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":1832,"label":1833,"description":1834,"key":60,"publicationTags":1835,"standard":18},[],{"EN":56,"VI":56},{"EN":58,"VI":59},[62,63],[66,67],{"id":69,"indexDatabase":1838,"url":80,"indexYears":81,"academicFieldIds":1843,"indexDatabaseRanking":85},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":1839,"label":1840,"description":1841,"key":77,"publicationTags":1842,"standard":18},[],{"EN":74,"VI":74},{"EN":74,"VI":76},[79],[83,84],{"impactFactor":19,"impactFactorByYear":1845,"i10Index":100,"i10IndexLast5Year":101,"totalPublication":102,"totalPublicationByYear":1846,"totalCitation":124,"totalCitationByYear":1847,"totalCitationPerPublication":143,"totalCitationPerPublicationByYear":1848,"hindexLast5Year":165,"hindex":165},{"2012":88,"2013":89,"2014":90,"2015":91,"2016":92,"2017":93,"2018":94,"2019":95,"2020":96,"2021":97,"2022":98,"2023":99},{"1995":104,"1996":105,"1997":106,"1998":106,"1999":107,"2000":108,"2001":109,"2002":110,"2003":105,"2004":108,"2005":107,"2006":111,"2007":112,"2008":113,"2009":114,"2010":104,"2011":115,"2012":107,"2013":116,"2014":117,"2015":114,"2016":118,"2017":119,"2018":120,"2019":121,"2020":114,"2021":117,"2022":113,"2023":122,"2024":123},{"1995":126,"1996":111,"2003":127,"2005":119,"2006":128,"2007":129,"2008":130,"2009":131,"2010":132,"2011":133,"2012":134,"2013":135,"2014":136,"2015":120,"2016":137,"2017":138,"2018":139,"2019":140,"2020":141,"2021":142},{"1995":145,"1996":146,"2003":147,"2005":148,"2006":149,"2007":150,"2008":151,"2009":152,"2010":153,"2011":154,"2012":155,"2013":156,"2014":157,"2015":158,"2016":159,"2017":160,"2018":161,"2019":162,"2020":163,"2021":164},"2017-08-07",[85,62],{"id":1852,"createTime":1853,"updateTime":1854,"relativeEntities":1855,"slug":1856,"properties":1857,"entityType":984,"verifyStatus":188,"verifyTime":1854,"verifyNote":985,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1866,"fullTextUrl":18,"authors":1867,"publicationType":1017,"publisherRelationship":1896,"citationCount":18,"citationInfo":18,"publishDate":1947,"publishYear":1948,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1949,"openAccess":18,"references":18,"isForceReanalyzing":1072},"03960def-35c6-4093-abec-9e613e7f35a7","2024-01-30T02:49:59.194+00:00","2025-02-11T13:16:24.051+00:00",[],"Comparing-Survival-Times-for-Treatments-with-Those-for-a-Control-Under-Proportional-Hazards",{"abstract":1858,"title":1860,"references":1862,"doi":1864},{"EN":1859},"Inferences for survival curves based on right censored data are studied for situations in which it is believed that the treatments have survival times at least as large as the control or at least as small as the control. Testing homogeneity with the appropriate order restricted alternative and testing the order restriction as the null hypothesis are considered. Under a proportional hazards model, the ordering on the survival curves corresponds to an ordering on the regression coefficients. Approximate likelihood methods, which are obtained by applying order restricted procedures to the estimates of the regression coefficients, and ordered analogues to the log rank test, which are based on the score statistics, are considered. Mau's (1988) test, which does not require proportional hazards, is extended to this ordering on the survival curves. Using Monte Carlo techniques, the type I error rates are found to be close to the nominal level and the powers of these tests are compared. Other order restrictions on the survival curves are discussed briefly.",{"EN":1861},"Comparing Survival Times for Treatments with Those for a Control Under Proportional Hazards",{"VOID":1863},"Arjas, Elja, “A graphical method for assessing the goodness of fit in Cox's proportional hazards model,” J. Amer. Statist. Assoc., vol. 83 pp. 204–212, 1988.\nBartholomew, D. J., “A test of homogeneity of means under restricted alternatives (with discussion),” J. R. Statist. Soc. B, vol. 23 pp. 239–281, 1961.\nBreslow, N., “A generalized Kruskal-Wallis test for comparing k samples subject to unequal pattern of censorship,” Biometrika, vol. 57 pp. 579–594, 1970.\nCox, D. R., “Regression models and life-tables (with discussions),” J. R. Statist. Soc. B, vol. 34 pp. 187–202, 1972.\nCox, D. R., “Partial likelihood,” Biometrika, vol. 62 pp. 269–276, 1975.\nDunnett, C.W., “A multiple comparisons procedure for comparing several treatments with a control,” J. Amer. Statist. Assoc., vol. 50 pp. 1096–1121, 1955.\nHorowitz, J.L. and Neumann, G.R., “A general moments specification test of the proportional hazards model,” J. Amer. Statist. Assoc., vol. 87 pp. 234–240, 1992.\nKudô, A., “A multivariate analogue of the one-sided test,” Biometrika, vol. 50 pp. 403–418, 1963.\nLawless, J. F., Statistical Models & Methods for Lifetime Data, Wiley: New York, 1982.\nLininger, L., Gail, M.H., Green, S.B. and Byar, D.P., “Comparison of four tests for equality of survival curves in the presence of stratification and censoring,” Biometrika, vol. 66 pp. 419–428, 1979.\nMau, Jochen, “A generalization of a nonparametric test for stochastically ordered distributions to censored survival data, J. R. Statist. Soc. B, vol. 50 pp. 403–412, 1988.\nRobertson, T., Wright, F. T. and Dykstra, R. L., Order Restricted Statistical Inference, Wiley: New York, 1988.\nSen, P. K., “Subhypotheses testing against restricted alternatives for the Cox regression model,” J. Statist. Planning and Inference, vol. 10 pp. 31–42, 1984.\nShapiro, A., “Asymptotic distribution of test statistics in the analysis of moment structures under inequality constraints,” Biometrika, vol. 72 pp. 133–140, 1985.\nSilvapulle, M. J., “On tests against one-sided hypotheses in some generalized linear models,” Biometrics, vol. 50 pp. 853–858, 1994.\nSilvapulle, M. J. and Silvapulle, P. “A score test against one-sided alternatives,” J. Amer. Statist. Assoc., vol. 90 pp. 342–349, 1995.\nSingh, B. and Wright, F.T., “Testing order restricted hypotheses with proportional hazards,” Lifetime Data Analysis, vol. 2 pp. 363–389, 1996.\nSteele, R.G.D. “A multiple comparisons rank sum test: treatment versus control,” Biometrics, vol. 15 pp. 560–572, 1959.\nTsiatis, A. A. “A large sample study of Cox's regression model,” Ann. Statist., vol. 9 pp. 93–108, 1981.",{"VOID":1865},"10.1023\u002FA:1009621915495","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1009621915495",[1868,1883],{"id":1869,"sortIndex":19,"researcher":18,"roles":1870,"affiliations":1871,"properties":1880,"displayName":1882,"givenName":18,"familyName":18},"c1bb9835-6479-4440-9812-5b563412fc37",[991],[1872],{"id":1873,"sortIndex":19,"affiliation":1874,"properties":18},"46a2aaa0-75bd-4094-af3d-800b9ff50550",{"id":1873,"createTime":18,"updateTime":18,"relativeEntities":1875,"slug":18,"properties":1876,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1879,"statistic":18},[],{"title":1877},{"VI":1878},"Department of Statistics, University of Missouri, Columbia",[],{"title":1881},{"VI":1882},"Bahadur Singh",{"id":1884,"sortIndex":200,"researcher":18,"roles":1885,"affiliations":1886,"properties":1893,"displayName":1895,"givenName":18,"familyName":18},"a29d0bd6-55c7-46ab-ae42-260e2b77d371",[991],[1887],{"id":1873,"sortIndex":19,"affiliation":1888,"properties":18},{"id":1873,"createTime":18,"updateTime":18,"relativeEntities":1889,"slug":18,"properties":1890,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1892,"statistic":18},[],{"title":1891},{"VI":1878},[],{"title":1894},{"VI":1895},"F. 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We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. 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J Comput Graph Stat 11(4):784–798\nCox C, Rothwell S, Madans J, Finucane F, Freid V, Kleinman J, Barbano H, Feldman J (1992) Plan and operation of the NHANES I Epidemiologic Followup Study, 1987. Vital Health Stat Ser 1(27):1–190\nDemnati A, Rao JNK (2010) Linearization variance estimators for model parameters from complex survey data. Surv Methodol 36(2):193–201\nDeville J (1999) Variance estimation for complex statistics and estimators: linearization and residual techniques. Surv Methodol 25(2):193–204\nEngel A, Murphy R, Maurer K, Collins E (1978) Plan and operation of the HANES I augmentation survey of adults 25–74 years United States, 1974–1975. Vital Health Stat Ser 1(14):1–110\nEzzati T, Massey J, Waksberg J, Chu A, Maurer K (1992) Sample design: third National Health and Nutrition Examination Survey. Vital Health Stat Ser 2(113):1–35\nFine J, Gray R (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509\nGraubard B, Korn E (2002) Inference for superpopulation parameters using sample surveys. Stat Sci 17(1):73–96\nGraubard BI, Fears TR (2005) Standard errors for attributable risk for simple and complex sample designs. Biometrics 61(3):847–855\nGray RJ (2009) Weighted analyses for cohort sampling designs. Lifetime Data Anal 15(1):24–40\nHampel FR (1974) Influence curve and its role in robust estimation. J Am Stat Assoc 69(346):383–393\nKalbfleisch JD, Lawless JF (1988) Likelihood analysis of multi-state models for disease incidence and mortality. Stat Med 7(1–2):149–160\nKish L, Frankel MR (1974) Inference from complex samples. J R Stat Soc Ser B Stat Methodol 36(1):1–22\nKorn EL, Graubard BI (1995) Examples of differing weighted and unweighted estimates from a sample survey. Am Stat 49(3):291–295\nKorn EL, Graubard BI (1999) Analysis of health surveys. Wiley series in probability and statistics. Wiley, New York\nLangholz B, Borgan O (1997) Estimation of absolute risk from nested case–control data. Biometrics 53(2):767–774\nLangholz B, Jiao J (2007) Computational methods for case–cohort studies. Comput Stat Data Anal 51(8):3737–3748\nLin D (2000) On fitting Cox’s proportional hazards models to survey data. Biometrika 87(1):37–47\nLin D, Wei L (1989) The robust inference for the Cox proportional hazards model. J Am Stat Assoc 84:1074–1078\nLumley T (2011) Survey: analysis of complex survey samples. R package version 3.26\nLumley TS (2004) Analysis of complex survey samples. J Stat Softw 9(1):1–19\nMcDowell A, Engel A, Massey J, Maurer K (1981) Plan and operation of the Second National Health and Nutrition Examination Survey, 1976–1980. Vital Health Stat Ser 1(15):1–114\nPatterson B, Dayton C, Graubard B (2002) Latent class analysis of complex sample survey data. J Am Stat Assoc 97(459):721–741\nPreston D, Lubin JH, Pierce D, McConney ME (1993) Epicure user’s guide. Hirosoft International Corporation, Seattle\nRao JNK, Scott AJ (1987) On simple adjustments to chi-square tests with sample survey data. Ann Stat 15(1):385–397\nReid N, Crepeau H (1985) Influence functions for proportional hazards regression. Biometrika 72(1):1–9\nSärndal CE, Swensson B, Wretman J (1992) Model assisted survey sampling. Springer series in statistics. Springer-Verlag, New York\nShah B (2002) Calculus of Taylor deviations. Joint Statistical Meetings, ASA, Minneapolis\nShen Y, Cheng SC (1999) Confidence bands for cumulative incidence curves under the additive risk model. Biometrics 55(4):1093–1100\nWilliams R (1995) Product-limit survival functions with correlated survival times. Lifetime Data Anal 1(2):171–186\nWoodruff RS (1971) Simple method for approximating variance of a complicated estimate. 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