Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phân tích sống Bayesian có thông tin
Tóm tắt
Chúng tôi cung cấp một cái nhìn tổng quan về ước lượng Bayesian, thử nghiệm giả thuyết và tính trung bình mô hình, và minh họa cách chúng mang lại lợi ích cho phân tích sống tham số. Chúng tôi so sánh khung Bayesian với cách tiếp cận tần suất hiện tại đang chiếm ưu thế và nhấn mạnh những lợi thế như việc tích hợp dữ liệu lịch sử một cách liền mạch, theo dõi liên tục bằng chứng và kết hợp sự không chắc chắn về quá trình sinh dữ liệu thực sự. Chúng tôi minh họa ứng dụng của các phương pháp Bayesian đã được đề cập trên một bộ dữ liệu ví dụ, phân tích lại một thử nghiệm ung thư đại tràng. Chúng tôi đánh giá hiệu suất của phân tích sống tham số Bayesian và các mô hình sống cực đại với việc lựa chọn mô hình AIC/BIC trong thiết kế cố định-n và thiết kế tuần tự thông qua một nghiên cứu mô phỏng. Trong việc phân tích lại có hồi cứu bộ dữ liệu ví dụ, khung Bayesian đã cung cấp bằng chứng về sự vắng mặt của hiệu ứng điều trị tích cực khi thêm Cetuximab vào phác đồ FOLFOX6 đối với sự sống không bệnh ở những bệnh nhân ung thư đại tràng giai đoạn III đã được cắt bỏ. Hơn nữa, phân tích tuần tự Bayesian sẽ kết thúc thử nghiệm sớm hơn 10.3 tháng so với phân tích tần suất tiêu chuẩn. Trong một nghiên cứu mô phỏng với các thiết kế tuần tự, khung Bayesian trung bình đã đưa ra quyết định trong gần một nửa thời gian yêu cầu bởi các đối tác tần suất, trong khi vẫn duy trì sức mạnh như nhau và tỷ lệ sai dương thích hợp. Dưới sự định nghĩa sai mô hình, khung Bayesian dẫn đến tỷ lệ sai âm cao hơn so với các đối tác tần suất, điều này dẫn đến tỷ lệ thử nghiệm chưa quyết định cao hơn. Trong các thiết kế cố định-n, khung Bayesian cho thấy sức mạnh nhỉnh hơn một chút, tỷ lệ sai số hơi tăng và độ thiên lệch thấp hơn cũng như RMSE khi ước lượng các hiệu ứng điều trị trong các mẫu nhỏ. Chúng tôi không tìm thấy sự khác biệt đáng kể cho các dự đoán sống. Chúng tôi đã làm cho phương pháp phân tích có sẵn cho các nhà nghiên cứu khác trong gói RoBSA R. Khung Bayesian được đề cập cung cấp nhiều lợi ích khi áp dụng cho các phân tích sống tham số. Nó sử dụng dữ liệu một cách hiệu quả hơn, có khả năng rút ngắn đáng kể thời gian của các thử nghiệm lâm sàng và cung cấp một tập hợp phong phú hơn về suy diễn.
Từ khóa
#Bayesian #phân tích sống #ước lượng #thử nghiệm giả thuyết #mô hình #thiết kế tuần tựTài liệu tham khảo
van de Schoot R, Depaoli S, King R, Kramer B, Märtens K, Tadesse MG, et al. Bayesian statistics and modelling. Nat Rev Methods Primers. 2021;1(1):1–26. https://doi.org/10.1038/s43586-020-00001-2.
Bayes T. An essay toward solving a problem in the doctrine of chances. By the late rev. mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S., 1763. Philos Trans R Soc Lond. 1997;53:370–418.
Jeffreys H. Some Tests of Significance, Treated by the Theory of Probability. Proceedings of the Cambridge Philosophy Society. 1935;31:203–222. Available from: https://doi.org/10.1017/S030500410001330X.
Wrinch D, Jeffreys H. On certain fundamental principles of scientific inquiry. Philosophical Magazine. 1921;42:369–90. https://doi.org/10.1080/14786442108633773.
Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd international workshop on distributed statistical computing. vol. 124. Vienna, Austria; 2003. p. 1–10.
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. JStat Softwe. 2017;76(1):1–32. https://doi.org/10.18637/jss.v076.i01.
Gronau QF, Singmann H, Wagenmakers EJ. bridgesampling: An R package for estimating normalizing constants. J Stat Softw. 2020;92(10):1–29. https://doi.org/10.18637/jss.v092.i10.
Gronau QF, Sarafoglou A, Matzke D, Ly A, Boehm U, Marsman M, et al. A tutorial on bridge sampling. J Math Psychol. 2017;81:80–97. https://doi.org/10.1016/j.jmp.2017.09.005.
Meng XL, Wong WH. Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica. 1996;6:831–60. Available from: https://www.jstor.org/stable/24306045.
Mills M. Introducing survival and event history analysis. London: Sage; 2010.
Klein RA, Vianello M, Hasselman F, Adams BG, Adams RB Jr, Alper S, et al. Many Labs 2: Investigating variation in replicability across samples and settings. Adv Methods Pract Psychol Sci. 2018;1(4):443–90. https://doi.org/10.1177/515245918810225.
Cox DR. Regression models and life-tables. J Royal Stat Soc Series B (Methodological). 1972;34(2):187–220. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.
Bogaerts K, Komárek A, Lesaffre E. Survival analysis with interval-censored data: A practical approach with examples in R, SAS, and BUGS. Boca Raton: Chapman and Hall/CRC; 2017.
Latimer NR. Survival analysis for economic evaluations alongside clinical trials–extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making: Int J Soc Medl Decis Making. 2013;33(6):743–54. https://doi.org/10.1177/0272989x12472398.
Breslow NE. Contribution to discussion of paper by DR Cox. J Royal Statist Soc Ser B. 1972;34:216–7.
Gronau QF, Ly A, Wagenmakers EJ. Informed Bayesian t-tests. Am Stat. 2020;74:137–43. https://doi.org/10.1080/00031305.2018.1562983.
Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol. 2015;68(1):52–60. https://doi.org/10.1016/%2Fj.jclinepi.2014.08.012.
Stefan AM, Evans NJ, Wagenmakers EJ. Practical challenges and methodological flexibility in prior elicitation. Psychol Methods. 2020; Available from: https://doi.org/10.1037/met0000354.
Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers EJ. Bayesian model-averaged meta-analysis in medicine. Stat Med. 2021;40(30):6743–61. https://doi.org/10.1002/sim.9170.
Parmar MKB, Sydes MR, Morris TP. How do you design randomised trials for smaller populations? A framework. BMC Med. 2016;14(1):183. https://doi.org/10.1186/s12916-016-0722-3.
Pocock SJ. The combination of randomized and historical controls in clinical trials. J Chronic Dis. 1976;29(3):175–88. https://doi.org/10.1016/0021-9681(76)90044-8.
Berry DA. Bayesian clinical trials. Nature Reviews Drug Discovery. 2006;5(1):27–36. https://doi.org/10.1038/nrd1927.
Hobbs BP, Carlin BP. Practical Bayesian design and analysis for drug and device clinical trials. J Biopharm Stat. 2008;18(1):54–80. https://doi.org/10.1080/10543400701668266.
Cope S, Ayers D, Zhang J, Batt K, Jansen JP. Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia. BMC Med Res Methodol. 2019;19(1):1–11. https://doi.org/10.1186/s12874-019-0823-8.
Thirard R, Ascione R, Blazeby JM, Rogers CA. Integrating expert opinions with clinical trial data to analyse low-powered subgroup analyses: a Bayesian analysis of the VeRDiCT trial. BMC Medical Res Methodol. 2020;20(1):1–10. https://doi.org/10.1186/s12874-020-01178-6.
Brard C, Hampson LV, Gaspar N, Le Deley MC, Le Teuff G. Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study. BMC Med Res Methodol. 2019;19(1):85. https://doi.org/10.1186/s12874-019-0714-z.
Hampson LV, Whitehead J, Eleftheriou D, Brogan P. Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Stat Med. 2014;33(24):4186–201. https://doi.org/10.1002/sim.6225.
Molinares C. Parametric and Bayesian modeling of reliability and survival analysis. Graduate Theses and Dissertations. 2011;Available from: https://scholarcommons.usf.edu/etd/3252.
Omurlu IK, Ture M, Ozdamar K. Bayesian analysis of parametric survival models: A computer simulation study based informative priors. J Stat Manag Syst. 2015;18(5):405–23. https://doi.org/10.1080/09720510.2014.961763.
van Rosmalen J, Dejardin D, van Norden Y, Löwenberg B, Lesaffre E. Including historical data in the analysis of clinical trials: Is it worth the effort? Stat Methods Med Res. 2018;27(10):3167–82. https://doi.org/10.1177/0962280217694506.
Viele K, Berry S, Neuenschwander B, Amzal B, Chen F, Enas N, et al. Use of historical control data for assessing treatment effects in clinical trials. Pharmaceutical Statistics. 2014;13(1):41–54. https://doi.org/10.1002/%2Fpst.1589.
Cuffe RL. The inclusion of historical control data may reduce the power of a confirmatory study. Statistics in Medicine. 2011;30(12):1329–38. https://doi.org/10.1002/sim.4212 PMID: 21432893.
Neuenschwander B, Capkun-Niggli G, Branson M, Spiegelhalter DJ. Summarizing historical information on controls in clinical trials. Clinical Trials (London, England). 2010;7(1):5–18. Available from: https://doi.org/10.1177/1740774509356002.
Johnson VE, Cook JD. Bayesian design of single-arm phase II clinical trials with continuous monitoring. Clin Trials. 2009;6(3):217–26. https://doi.org/10.1177/1740774509105221.
Berger JO, Wolpert RL. The likelihood principle. 2nd ed. Institute of Mathematical Statistics; 1988.
Rouder JN. Optional stopping: No problem for Bayesians. Psych Bulletin Rev. 2014;21(2):301–8. https://doi.org/10.3758/s13423-014-0595-4.
Wagenmakers EJ, Wetzels R, Borsboom D, van der Maas HLJ, Kievit RA. An agenda for purely confirmatory research. Perspect Psychol Sci. 2012;7(6):632–8. https://doi.org/10.1177/1745691612463078.
Goodman SN. Introduction to Bayesian methods I: measuring the strength of evidence. Sage Publications Sage CA: Thousand Oaks, CA; 2005. https://doi.org/10.1191/1740774505cn098oa.
Robbins H. Some aspects of the sequential design of experiments. Bulletin of the Ame Math Soc. 1952;58(5):527–35. https://doi.org/10.1007/978-1-4612-5110-1_13.
Anscombe FJ. Fixed-sample-size analysis of sequential observations. Biometrics. 1954;10(1):89–100. https://doi.org/10.2307/3001665.
Cornfield J. Sequential trials, sequential analysis and the likelihood principle. Am Stat. 1966;20(2):18–23. https://doi.org/10.1080/00031305.1966.10479786.
Lan GKK, DeMets DL. Discrete sequential boundaries for clinical trials. Biometrika. 1983;70(3):659–63. https://doi.org/10.1093/biomet/70.3.659.
O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979;p. 549–556. https://doi.org/10.2307/2530245.
Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64(2):191–199. Publisher: [Oxford University Press, Biometrika Trust]. https://doi.org/10.1093/biomet/64.2.191.
Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med. 2020;18(1):1–21. https://doi.org/10.1186/s12916-020-01808-2.
Schnuerch M, Erdfelder E. Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychol Methods. 2020;25(2):206. https://doi.org/10.1037/met0000234.
Schönbrodt FD, Wagenmakers EJ, Zehetleitner M, Perugini M. Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychol Methods. 2017;22(2):322. https://doi.org/10.1037/met0000061.
Wald A. Sequential tests of statistical hypotheses. Ann Math Stat. 1945;16(2):117–86. https://doi.org/10.1007/978-1-4612-0919-5_18.
Berry DA. Interim analyses in clinical trials: Classical vs Bayesian approaches. Stat Med. 1985;4(4):521–6. https://doi.org/10.1002/sim.4780040412.
Ibrahim JG, Chen MH, Sinha D. Bayesian survival analysis. Springer Series in Statistics. New York: Springer-Verlag; 2001. Available from: https://doi.org/10.1007/978-1-4757-3447-8.
Hinne M, Gronau QF, van den Bergh D, Wagenmakers EJ. A conceptual introduction to Bayesian model averaging. Adv Methods Pract Psychol Sci. 2020;3(2):200–15. https://doi.org/10.1177/2515245919898657.
Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: a tutorial. Stat Sci. 1999;14(4):382–401. https://doi.org/10.1214/SS\%2F1009212519.
Leamer EE. Specification searches: Ad hoc inference with nonexperimental data, vol. 53. New York: Wiley; 1978.
Kleinbaum DG, Klein M. Survival analysis: A self-learning text, third edition. 3rd ed. Statistics for Biology and Health. New York: Springer-Verlag; 2012. https://doi.org/10.1007/978-1-4419-6646-9.
Latimer NR. NICE DSU technical support document 14: Survival analysis for economic evaluations alongside clinical trials-extrapolation with patient-level data. Report by the Decision Support Unit. 2011;Available from: https://www.ncbi.nlm.nih.gov/books/NBK395885/pdf/Bookshelf_NBK395885.pdf.
Buckland ST, Burnham KP, Augustin NH. Model selection: An integral part of inference. Biometrics. 1997;53(2):603–618. Publisher: [Wiley, International Biometric Society]. https://doi.org/10.2307/2533961.
Hjort NL, Claeskens G. Frequentist model average estimators. J Am Stat Assoc. 2003;98(464):879–99. https://doi.org/10.1198/016214503000000828.
Wagenmakers EJ, Farrell S. AIC model selection using Akaike weights. Psychonomic Bulletin & Rev. 2004;11(1):192–6. https://doi.org/10.3758/BF03206482.
Bell Gorrod H, Kearns B, Stevens J, Thokala P, Labeit A, Latimer N, et al. A review of survival analysis methods used in NICE technology appraisals of cancer treatments: Consistency, limitations, and areas for improvement. Med Decis Making. 2019;39(8):899–909. https://doi.org/10.1177/0272989x19881967.
Kearns B, Stevens J, Ren S, Brennan A. How uncertain is the survival extrapolation? A study of the impact of different parametric survival models on extrapolated uncertainty about hazard functions, lifetime mean survival and cost effectiveness. Pharmacol Econ. 2020;38(2):193–204. https://doi.org/10.1007/s40273-019-00853-x.
Ishak KJ, Kreif N, Benedict A, Muszbek N. Overview of parametric survival analysis for health-economic applications. PharmacoEconomics. 2013;31(8):663–75. https://doi.org/10.1007/s40273-013-0064-3.
Brard C, Le Teuff G, Le Deley MC, Hampson LV. Bayesian survival analysis in clinical trials: What methods are used in practice? Clin Trials. 2017;14(1):78–87. https://doi.org/10.1177/1740774516673362.
Wadsworth I, Hampson LV, Jaki T. Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods. Statist Methods Med Res. 2018;27(2):398–413. https://doi.org/10.1177/0962280216631359.
Stallard N, Todd S, Ryan EG, Gates S. Comparison of Bayesian and frequentist group-sequential clinical trial designs. BMC Med Res Methodol. 2020;20(1):1–14. https://doi.org/10.1186/s12874-019-0892-8.
Negrín MA, Nam J, Briggs AH. Bayesian solutions for handling uncertainty in survival extrapolation. Med Decis Making. 2017;37(4):367–76. https://doi.org/10.1177/0272989x16650669.
Thamrin SA, McGree JM, Mengersen KL. Modelling survival data to account for model uncertainty: a single model or model averaging? SpringerPlus. 2013;2(1):665. https://doi.org/10.1186/2193-1801-2-665.
Gallacher D, Auguste P, Connock M. How do pharmaceutical companies model survival of cancer patients? A review of NICE single technology appraisals in 2017. Int J Technol Assess Health Care. 2019;35(2):160–7. https://doi.org/10.1017/s0266462319000175.
O’Hagan A. Science, subjectivity and software (comment on articles by Berger and by Goldstein). Bayesian Analysis. 2006;1(3):445–50. https://doi.org/10.1214/06-BA116G.
Giovagnoli A. The Bayesian design of adaptive clinical trials. Int J Environ Res Public Health. 2021;18(2):1–15. https://doi.org/10.3390/IJERPH18020530.
Alberts SR, Sargent DJ, Nair S, Mahoney MR, Mooney M, Thibodeau SN, et al. Effect of Oxaliplatin, Fluorouracil, and Leucovorin with or without Cetuximab on survival among patients with resected stage III colon cancer: A randomized trial. JAMA. 2012;307(13):1383–93. https://doi.org/10.1001/jama.2012.385.
Bartoš F. RoBSA: An R package for robust Bayesian survival-analyses; 2022. CRAN. Available from: https://CRAN.R-project.org/package=RoBSA.
Fragoso TM, Bertoli W, Louzada F. Bayesian model averaging: A systematic review and conceptual classification. Int Stat Rev. 2018;86(1):1–28. https://doi.org/10.1111/insr.12243.
Jefferys WH, Berger JO. Ockham’s razor and Bayesian analysis. American Scientist. 1992;80:64–72. Available from: http://www.jstor.org/stable/29774559.
Jeffreys H. Scientific inference. Cambridge: Cambridge University Press; 1931.
Jeffreys H. Theory of probability. 1st ed. Oxford, UK: Oxford University Press; 1939.
Etz A, Wagenmakers EJ. JBS Haldane’s contribution to the Bayes factor hypothesis Test. Stat Sci. 2017;32:313–29. https://doi.org/10.1214/16-STS599.
Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–95. https://doi.org/10.1080/01621459.1995.10476572.
Rouder JN, Morey RD. Teaching Bayes’ theorem: Strength of evidence as predictive accuracy. Am Stat. 2019;73(2):186–90. https://doi.org/10.1080/00031305.2017.1341334.
Wagenmakers EJ, Morey RD, Lee MD. Bayesian benefits for the pragmatic researcher. Curr Dir Psychol Sci. 2016;25(3):169–76. https://doi.org/10.1177/0963721416643289.
Box GE. Science and statistics. J Am Stat Assoc. 1976;71(356):791–9. https://doi.org/10.1080/01621459.1976.10480949.
Gronau QF, Heck DW, Berkhout SW, Haaf JM, Wagenmakers EJ. A primer on Bayesian model-averaged meta-analysis. Advances in Methods and Practices in Psychological Science. 2021;4(3). Available from: https://doi.org/10.1177/%2F25152459211031256.
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Chichester: John Wiley & Sons; 2004.
Goodman SN. Toward evidence-based medical statistics. 2: The Bayes factor. Ann Intern Med. 1999;130(12):1005–13. https://doi.org/10.7326/0003-4819-130-12-199906150-00019.
Goodman SN. Of p-values and Bayes: a modest proposal. Epidemiol. 2001;12(3):295–7. https://doi.org/10.1097/00001648-200105000-00006.
Neyman J, Pearson ES. Contributions to the theory of testing statistical hypotheses. Statistical Research Memoirs. 1936(1):1–37.
Spiegelhalter DJ, Freedman LS, Parmar MK. Bayesian approaches to randomized trials. J R Stat Soc: Series A (Statistics in Society). 1994;157(3):357–87. https://doi.org/10.2307/2983527.
Kruschke JK. Bayesian estimation supersedes the t test. J Exp Psychol Gen. 2013;142(2):573. https://doi.org/10.1037/a0029146.
Edwards W, Lindman H, Savage LJ. Bayesian statistical inference for psychological research. Psychol Rev. 1963;70(3):193–242. https://doi.org/10.1037/h0044139.
Rouder JN, Haaf JM, Aust F. From theories to models to predictions: A Bayesian model comparison approach. Commun Monogr. 2018;85(1):41–56. https://doi.org/10.1080/03637751.2017.1394581.
Royall R. On the probability of observing misleading statistical evidence. J Am Stat Assoc. 2000;95(451):760–8. https://doi.org/10.1080/01621459.2000.10474264.
Altman DG. Statistics in medical journals. Stat Med. 1982;1(1):59–71. https://doi.org/10.1002/sim.4780010109.
Altman DG. Statistics in medical journals: developments in the 1980s. Stat Med. 1991;10(12):1897–913. https://doi.org/10.1002/sim.4780101206.
Altman DG. Statistics in medical journals: some recent trends. Stat Med. 2000;19(23):3275–89. https://doi.org/10.1002/1097-0258(20001215)19:23\%3C3275::aid-sim626\%3E3.0.co;2-m.
Schönbrodt FD, Wagenmakers EJ. Bayes factor design analysis: Planning for compelling evidence. Psychon Bull Rev. 2018;25(1):128–42. https://doi.org/10.3758/s13423-017-1230-y.
Stefan AM, Gronau QF, Schönbrodt FD, Wagenmakers EJ. A tutorial on Bayes factor design analysis using an informed prior. Behav Res Methods. 2019;51(3):1042–58. https://doi.org/10.3758/s13428-018-01189-8.
Berger JO. Statistical decision theory and Bayesian analysis. New York: Springer Science & Business Media; 2013.
Ly A, Verhagen J, Wagenmakers EJ. Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. J Math Psychol. 2016;72:19–32. https://doi.org/10.1016/j.jmp.2015.06.004.
Jennison C, Turnbull BW. Group sequential methods with applications to clinical trials. Boca Raton: CRC Press; 1999.
Org R. Project Data Sphere; 2019. Publisher: re3data.org - Registry of Research Data Repositories. Available from: https://www.re3data.org/repository/r3d100013015.
Alliance for Clinical Trials in Oncology. A randomized phase III trial of Oxaliplatin (OXAL) plus 5-Fluorouracil (5-FU)/Leucovorin (CF) with or without Cetuximab (C225) after curative resection for patients with stage III colon cancer; 2012. https://doi.org/10.34949/zywx-9253.
Alberts SR, Sinicrope FA, Grothey A. N0147: a randomized phase III trial of oxaliplatin plus 5-fluorouracil/leucovorin with or without cetuximab after curative resection of stage III colon cancer. Clin Colorectal Cancer. 2005;5(3):211–3. https://doi.org/10.3816/ccc.2005.n.033.
Raftery AE, Madigan D, Volinsky CT. Accounting for model uncertainty in survival analysis improves predictive performance. In: In Bayesian Statistics 5. University Press; 1995. p. 323–349.
Bartlett M. A comment on D V Lindleys statistical paradox. Biometrika. 1957;44(34):533–533. https://doi.org/10.1093/biomet/44.3-4.533.
Madigan D, Raftery AE, York JC, Bradshaw JM, Almond RG. Strategies for graphical model selection. Lecture Notes in Statistics. New York: Springer; 1994. p. 91–100.
Gronau QF, van Erp S, Heck DW, Cesario J, Jonas KJ, Wagenmakers EJ. A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Compr Results Social Psychol. 2017;2(1):123–38. https://doi.org/10.1080/23743603.2017.1326760.
Maier M, Bartoš F, Wagenmakers EJ. Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods. 2022. https://doi.org/10.1037/met0000405.
Bartoš F, Maier M, Wagenmakers EJ, Doucouliagos H, Stanley TD. Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods. Research Synthesis Methods; in press. https://doi.org/10.31234/osf.io/kvsp7.
Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press; 2006.
Alliance for Clinical Trials in Oncology. Phase III randomized study of adjuvant immunotherapy with monoclonal antibody 17–1A versus no adjuvant therapy following resection for state II (modified Astler-Coller B2) adenocarcinoma of the colon (comparator arm); 2003. https://doi.org/10.34949/y9s0-nz36.
Sanofi. Multicenter international study of Oxaliplatin/ 5FU-LV in the adjuvant treatment of colon cancer; 2003. https://doi.org/10.34949/fm7n-cw30.
Alliance for Clinical Trials in Oncology. Phase III randomized study of adjuvant immunotherapy with monoclonal antibody 17–1A versus no adjuvant therapy following resection for state II (modified Astler-Coller B2) adenocarcinoma of the colon (experimental arm); 2003. https://doi.org/10.34949/57rt-nr42.
Lee MD, Wagenmakers EJ. Bayesian cognitive modeling: A practical course. New York: Cambridge University Press; 2013.
Denwood MJ. runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software. 2016;71(9):1–25. Available from: https://doi.org/10.18637/jss.v071.i09.
Royston P, Parmar MK. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–97. https://doi.org/10.1002/sim.1203.
Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019;38(11):2074–102. https://doi.org/10.1002/sim.8086.
Efron B, Stein C. The jackknife estimate of variance. Ann Stat. 1981;9(3):586–96. https://doi.org/10.1214/aos/1176345462.
Hwang IK, Shih WJ, De Cani JS. Group sequential designs using a family of type I error probability spending functions. Stat Med. 1990;9(12):1439–45. https://doi.org/10.1002/sim.4780091207.
Anderson KM. Optimal spending functions for asymmetric group sequential designs. Biom J: J Math Methods in Biosci. 2007;49(3):337–45. https://doi.org/10.1002/bimj.200510205.
Rouder JN, Morey RD, Verhagen J, Province JM, Wagenmakers EJ. Is there a free lunch in inference? Top Cogn Sci. 2016;8(3):520–47. https://doi.org/10.1111/tops.12214.
Johnson SR, Tomlinson GA, Hawker GA, Granton JT, Feldman BM. Methods to elicit beliefs for Bayesian priors: a systematic review. J Clin Epidemiol. 2010;63(4):355–69. https://doi.org/10.1016/j.jclinepi.2009.06.003.
Chaloner K. The elicitation of prior distributions. In Case Studies in Bayesian Biostatistics. 1996; 141-156.
O’Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ, et al. Uncertain judgements: eliciting experts’ probabilities. Chichester: Wiley; 2006.
Mikkola P, Martin OA, Chandramouli S, Hartmann M, Pla OA, Thomas O, et al. Prior knowledge elicitation: The past, present, and future; 2021. Available from: https://arxiv.org/abs/2112.01380.
Wagenmakers EJ, Ly A. History and nature of the Jeffreys-Lindley paradox; 2021. Available from:https://arxiv.org/abs/2111.10191.
Benjamin DJ, Berger JO, Johannesson M, Nosek BA, Wagenmakers EJ, Berk R, et al. Redefine statistical significance. Nat Hum Behav. 2018;2(1):6–10. https://doi.org/10.1038/s41562-017-0189-z.
Maier M, Lakens D. Justify your alpha: A primer on two practical approaches; 2021. Available from: https://doi.org/10.31234/osf.io/ts4r6.
Wagenmakers EJ. Approximate objective Bayes factors from p-values and sample size: The 3p√n rule; 2022. Available from: https://doi.org/10.31234/osf.io/egydq.
Good I. Standardized tail-area probabilities. J Stat Comput Simul. 1982;16(1):65–6. https://doi.org/10.1080/00949658208810607.
Lindley DV. Statistical inference. J R Stat Soc: Series B (Methodological). 1953;15(1):30–65. https://doi.org/10.1111/j.2517-6161.1953.tb00123.x.
Ramsay JO. Monotone regression splines in action. Stat Sci. 1988;3(4):425–41. https://doi.org/10.1214/ss/1177012761.
Brilleman SL, Elci EM, Novik JB, Wolfe R. Bayesian survival analysis using the rstanarm R package. arXiv:200209633 [stat]. 2020 2;ArXiv: 2002.09633. Available from: http://arxiv.org/abs/2002.09633.
King AJ, Weiss RE. A general semiparametric Bayesian discrete-time recurrent events model. Biostatistics. 2019;(kxz029). Available from: https://doi.org/10.1093/biostatistics/kxz029.
Sinha D, Dey DK. Semiparametric Bayesian analysis of survival data. J Am Stat Assoc. 1997;92(439):1195–212. https://doi.org/10.1080/01621459.1997.10474077.
Held L, Sabanés Bové D. Applied statistical inference. New York: Springer; 2014.