Các loại thành kiến tiềm năng khi ước lượng ảnh hưởng nguyên nhân trong nghiên cứu môi trường và cách giải thích chúng

Springer Science and Business Media LLC - Tập 13 - Trang 1-31 - 2024
Ko Konno1, James Gibbons1, Ruth Lewis2, Andrew S Pullin1
1School of Natural Sciences, Bangor University, Bangor, UK
2School of Medical and Health Sciences, Bangor University, Bangor, UK

Tóm tắt

Để thông tin cho chính sách và thực tiễn môi trường, các nhà nghiên cứu ước lượng ảnh hưởng của can thiệp/exposure bằng cách tiến hành nghiên cứu ban đầu (ví dụ, đánh giá tác động) hoặc nghiên cứu thứ cấp (ví dụ, đánh giá chứng cứ). Nếu các ước lượng này được xuất phát từ nghiên cứu được thực hiện/ báo cáo kém, thì chúng có thể gây ra sự hiểu lầm trong việc chính sách và thực tiễn bằng cách cung cấp các ước lượng thiên lệch. Nhiều loại thiên lệch đã được mô tả, đặc biệt trong khoa học sức khỏe và y tế. Chúng tôi nhằm mục đích lập bản đồ tất cả các loại thiên lệch từ tài liệu có liên quan đến việc ước lượng ảnh hưởng nguyên nhân trong lĩnh vực môi trường. Tất cả các loại thiên lệch đã được xác định ban đầu bằng cách sử dụng Danh mục Thiên lệch (catalogofbias.org) và xem xét các ấn phẩm chính (n = 11) đã tập hợp và mô tả các thiên lệch trước đây. Chúng tôi đã xác định được 121 (trong tổng số 206) loại thiên lệch có liên quan đến việc ước lượng ảnh hưởng nguyên nhân trong lĩnh vực môi trường. Chúng tôi cung cấp một diễn giải tổng quan của mọi loại thiên lệch có liên quan được bao phủ bởi bảy miền rủi ro- thiên lệch cho nghiên cứu ban đầu: rủi ro thiên lệch do nhầm lẫn; rủi ro thiên lệch do lựa chọn hậu can thiệp/exposure; rủi ro thiên lệch so sánh do phân loại/misclassified; rủi ro thiên lệch về hiệu suất; rủi ro thiên lệch phát hiện; rủi ro thiên lệch báo cáo kết quả; rủi ro thiên lệch đánh giá kết quả, và bốn miền cho nghiên cứu thứ cấp: rủi ro thiên lệch tìm kiếm; rủi ro thiên lệch sàng lọc; rủi ro thiên lệch đánh giá nghiên cứu và mã hóa/trích xuất dữ liệu; rủi ro thiên lệch tổng hợp dữ liệu. Sự tập hợp của chúng tôi nên giúp các nhà khoa học và nhà quyết định trong lĩnh vực môi trường nhận thức tốt hơn về bản chất của thiên lệch trong ước lượng các ảnh hưởng nguyên nhân. Nghiên cứu trong tương lai là cần thiết để chính thức hóa định nghĩa của các loại thiên lệch đã được tập hợp, chẳng hạn như thông qua phân rã sử dụng các công thức toán học.

Từ khóa

#thiên lệch #ước lượng ảnh hưởng nguyên nhân #nghiên cứu môi trường #chính sách môi trường #can thiệp

Tài liệu tham khảo

Kirkham JJ, Dwan KM, Altman DG, Gamble C, Dodd S, Smyth R, et al. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. BMJ (Online). 2010;340: c365. Fleming PS, Koletsi D, Dwan K, Pandis N. Outcome discrepancies and selective reporting: Impacting the leading journals? PLoS ONE. 2015;10: e0127495. Hart B, Lundh A, Bero L. Effect of reporting bias on meta-analyses of drug trials: Reanalysis of meta-analyses. BMJ. 2012;344: d7202. Jones CW, Keil LG, Holland WC, Caughey MC, Platts-Mills TF. Comparison of registered and published outcomes in randomized controlled trials: a systematic review. BMC Med. 2015;13:1–12. Fox MP, MacLehose RF, Lash TL. Applying quantitative bias analysis to epidemiologic data. 2nd ed. Cham: Springer; 2021. Eisenhart C. Expression of the uncertainties of final results. Science. 1979;1968(160):1201–4. Everitt BS, Skrondal A. The Cambridge dictionary of statistics. The Cambridge Dictionary of Statistics; 2010. Suzuki E, Tsuda T, Mitsuhashi T, Mansournia MA, Yamamoto E. Errors in causal inference: an organizational schema for systematic error and random error. Ann Epidemiol. 2016;26:788-793.e1. Cochran W. Sampling techniques. 3rd ed. New York: John Wiley and Sons; 1977. Upton G, Cook I. A dictionary of statistics. 3rd ed. New York: Oxford University Press; 2014. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.0. Cochrane Handbook for Systematic Reviews of Interventions. Cochrane; 2019. www.training.cochrane.org/handbook Collaboration for Environmental Evidence. Guidelines and Standards for Evidence Synthesis in Environmental Management Version 5.0. 2018; Frampton G, Whaley P, Bennett M, Bilotta G, Dorne JLCM, Eales J, et al. Principles and framework for assessing the risk of bias for studies included in comparative quantitative environmental systematic reviews. Environ Evid. 2022;11:12. Mahtani K, Spencer E, Brassey J. Observer bias. Catalogue of bias; 2017. https://catalogofbias.org/biases/observer-bias/. Accessed 5 Oct 2021. O’Sullivan J, Banerjee A, Pluddemann A. Detection bias. Catalogue of Bias. 2017 [cited 2021 Oct 5]. https://catalogofbias.org/biases/detection-bias/. Accessed 5 Oct 2021. Pearl J. Causality. 2nd ed. New York: Cambridge University Press; 2009. Hernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC; 2020. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Ma LL, Wang YY, Yang ZH, Huang D, Weng H, Zeng XT. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Mil Med Res. 2020;7:1–11. Pullin AS, Knight TM. Effectiveness in conservation practice: pointers from medicine and public health. Conserv Biol. 2001;15:50–4. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. The BMJ. 2019;366: l4898. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ (Online). 2016;355: i4919. Whiting P, Savović J, Higgins JPT, Caldwell DM, Reeves BC, Shea B, et al. ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol. 2016;69:225–34. Barker TH, Stone JC, Sears K, Klugar M, Tufanaru C, Leonardi-Bee J, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials. JBI Evid Synth. 2023;21:494–506. Barker TH, Stone JC, Sears K, Klugar M, Leonardi-Bee J, Tufanaru C, et al. Revising the JBI quantitative critical appraisal tools to improve their applicability: an overview of methods and the development process. JBI Evid Synth. 2023;21:478–93. Stanhope J, Weinstein P. Critical appraisal in ecology: What tools are available, and what is being used in systematic reviews? Res Synth Methods. 2022;14:342–56. Zvereva EL, Kozlov MV. Biases in ecological research: attitudes of scientists and ways of control. Sci Rep. 2021;11:342–56. Nunan D, Aronson J, Bankhead C. Catalogue of bias: attrition bias. BMJ Evid Based Med. 2018;23:21–2. Sackett DL. Bias in analytic research. J Chronic Dis. 1979;32:51–63. Bayliss HR, Beyer FR. Information retrieval for ecological syntheses. Res Synth Methods. 2015;6:136–48. Clarke M, Atkinson P, Badenoch D, Chalmers I, Glasziou P, Podolsky S, et al. The James Lind library’s introduction to fair tests of treatments. www.jameslindlibrary.org Smith J, Noble H. Bias in research. Evidence Based. Nursing. 2014;17:100–1. Thakur A, Choudhury V, Saluja S. Bias in research. Curr Med Res Pract. 2012;2:106–10. Paradis C. Bias in surgical research. Ann Surg. 2008;248:180–8. Warden G. Definitions of bias in clinical research. Clin Epidemiol. 2015;2249:31–48. Delgado-Rodriguez M, Llorca J. Bias. J Epidemiol Community Health (1978). 2004;58:635–41. Hartman JM, Forsen JW, Wallace MS, Neely JG. Tutorials in clinical research: Part IV: recognizing and controlling bias. Laryngoscope. 2002;112:23–31. Marchevsky D. Bias. Critical appraisal of medical literature. Springer Science & Business Media; 2000. p. 57–61. Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126:619. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22:276–82. Porta M. A dictionary of epidemiology. 6th ed. New York: Oxford University Press; 2014. Hernán MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health. 1978;2004(58):265–71. Steenland K, Schubauer-Berigan MK, Vermeulen R, Lunn RM, Straif K, Zahm S, et al. Risk of bias assessments and evidence syntheses for observational epidemiologic studies of environmental and occupational exposures: Strengths and limitations. Environ Health Perspect. 2020;128: 095002. Cochran W, Cox G. Experimental designs. 2nd ed. New York: John Wiley & Sons; 1957. Collaboration for Environmental Evidence. Guidelines and Standards for Evidence Synthesis in Environmental Management Version 5.1. 2022. 2022 . https://environmentalevidence.org/information-for-authors/guidelines-for-authors/. Accessed 3 Mar 2023. Konno K, Cheng SH, Eales J, Frampton G, Kohl C, Livoreil B, et al. The CEEDER database of evidence reviews: an open-access evidence service for researchers and decision-makers. Environ Sci Policy. 2020;114:256–62. Woodcock P, Pullin AS, Kaiser MJ. Evaluating and improving the reliability of evidence syntheses in conservation and environmental science: a methodology. Biol Conserv. 2014;176:54–62. Spencer E, Heneghan C. All’s well literature bias. Catalogue of Bias. 2018. https://catalogofbias.org/biases/alls-well-literature-bias/. Accessed 5 Oct 2021. Spencer E, Heneghan C, Nunan D. Allocation bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/allocation-bias/. Accessed 3 Apr 2022. Spencer E, Brassey J. Ascertainment bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/ascertainment-bias/. Accessed 5 Oct 2021. Spencer E, Mahtani K. Hawthorne effect. Catalogue of Bias. 2017. https://catalogofbias.org/biases/hawthorne-effect/. Accessed 5 Oct 2021. Bankhead C, Aronson J, Nunan D. Attrition bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/attrition-bias. Accessed 5 Oct 2021. Banerjee A, Nunan D. Availability bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/availability-bias/. Accessed 5 Oct 2021. Spencer E, Heneghan C. Chronological bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/chronological-bias/. Accessed 5 Oct 2021. Lee H, Aronson J, Nunan D. Collider bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/collider-bias/. Accessed 5 Oct 2021. Spencer E, Heneghan C. Compliance bias. Catalogue of Bias. 2018. https://catalogofbias.org/biases/compliance-bias/. Accessed 5 Oct 2021. Spencer E, Heneghan C. Confirmation bias. Catalogue of Bias. 2018. https://catalogofbias.org/biases/confirmation-bias/. Accessed 5 Oct 2021. Aronson J, Bankhead C, Nunan D. Confounding. Catalogue of Bias. 2018. https://catalogofbias.org/biases/confounding/. Accessed 5 Oct 2021. Aronson J, Bankhead C, Mahtani K, Nunan D. Confounding by indication. Catalogue of Bias. 2018. https://catalogofbias.org/biases/confounding-by-indication/. Accessed 5 Oct 2021. Erasmus A, Holman B, Ioannidis J. Data-dredging bias. Catalogue of Bias. 2020. https://catalogofbias.org/biases/data-dredging-bias/. Accessed 5 Oct 2021. Aronson J, Bankhead C, Nunan D. Hot stuff bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/hot-stuff-bias/. Accessed 5 Oct 2021. Holman B, Bero L, Mintzes B. Industry sponsorship bias. Catalogue of Bias. 2019 [cited 2021 Oct 5]. https://catalogofbias.org/biases/industry-sponsorship-bias/ Bankhead C, Spencer E, Nunan D. Information bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/information-bias/. Accessed 5 Oct 2021. Heneghan C, Brassey J. Insensitive measure bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/insensitive-measure-bias/. Accessed 5 Oct 2021. Nunan D, Heneghan C. Lack of blinding. Catalogue of Bias. 2018. https://catalogofbias.org/biases/lack-of-blinding/. Accessed 5 Oct 2021. Brassey J, Spencer E, Heneghan C. Language bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/language-bias/. Accessed 5 Oct 2021. Spencer E, Mahtani K, Brassey J, Heneghan C. Misclassification bias. Catalogue of Bias. 2018. https://catalogofbias.org/biases/misclassification-bias/. Accessed 5 Oct 2021. Spencer E, Brassey J, Heneghan C. Non-contemporaneous control bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/non-contemporaneous-control-bias/. Accessed 5 Oct 2021. Turk A, Heneghan C, Nunan D. Non-response bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/non-response-bias/. Accessed 5 Oct 2021. Persaud N, Heneghan C. Novelty bias. Catalogue of Bias. 2021. https://catalogofbias.org/biases/novelty-bias/. Accessed 5 Oct 2021. Spencer E, Brassey J, Heneghan C. One-sided reference bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/one-sided-reference-bias/. Accessed 5 Oct 2021. Thomas E, Heneghan C. Outcome reporting bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/outcome-reporting-bias/. Accessed 5 Oct 2021. Spencer E, Brassey J. Perception bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/perception-bias. Accessed 5 Oct 2021. Banerjee A, Pluddemann A, O’Sullivan J, Nunan D. Performance bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/performance-bias/. Accessed 5 Oct 2021. Plüddemann A, Banerjee A, O’Sullivan J. Positive results bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/positive-results-bias/. Accessed 5 Oct 2021. Spencer E, Heneghan C. Prevalence-incidence (Neyman) bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/prevalence-incidence-neyman-bias/. Accessed 5 Oct 2021. Devito N, Goldacre B. Publication bias. Catalogue of Bias. 2019. Spencer E, Brassey J, Mahtani K. Recall bias. 2017 [cited 2021 Oct 5]. https://catalogofbias.org/biases/recall-bias/. Accessed 5 Oct 2021. Richards G, Onakpoya I. Reporting biases. Catalogue of Bias. 2019. https://catalogofbias.org/biases/reporting-biases/. Accessed 5 Oct 2021. Nunan D, Bankhead C, Aronson J. Selection bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/selection-bias/. Accessed 5 Oct 2021. Mahtani K, Chalmers I, Nunan D. Spin bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/spin-bias/. Accessed 5 Oct 2021. Heneghan C. Starting time bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/starting-time-bias. Accessed 5 Oct 2021. Heneghan C, Spencer E. Substitution game bias. Catalogue of Bias. 2019. https://catalogofbias.org/biases/substitution-game-bias/. Accessed 5 Oct 2021. Brassey J, Mahtani K, Spencer E, Heneghan C. Volunteer bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/volunteer-bias/. Accessed 5 Oct 2021. Spencer E, Brassey J, Mahtani K, Heneghan C. Wrong sample size bias. Catalogue of Bias. 2017. https://catalogofbias.org/biases/wrong-sample-size-bias/. Accessed 5 Oct 2021. Bero L, Chartres N, Diong J, Fabbri A, Ghersi D, Lam J, et al. The risk of bias in observational studies of exposures (ROBINS-E) tool: concerns arising from application to observational studies of exposures. Syst Rev. 2018;7:242. Hudson P, Botzen WJW, Kreibich H, Bubeck P, Aerts JCJH. Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching. Nat Hazard. 2014;14:1731–47. Takeshita KM, Hayashi TI, Yokomizo H. The effect of intervention in nickel concentrations on benthic macroinvertebrates: a case study of statistical causal inference in ecotoxicology. Environ Pollut. 2020;265: 115059. Konno K, Akasaka M, Koshida C, Katayama N, Osada N, Spake R, et al. Ignoring non-English-language studies may bias ecological meta-analyses. Ecol Evol. 2020;10:6373–84. Konno K, Pullin AS. Assessing the risk of bias in choice of search sources for environmental meta-analyses. Res Synth Methods. 2020. https://doi.org/10.1002/jrsm.1433. Pullin AS, Cheng SH, Jackson JD, Eales J, Envall I, Fada SJ, et al. Standards of conduct and reporting in evidence syntheses that could inform environmental policy and management decisions. Environ Evid. 2022;11:16. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74. Pullin AS, Stewart G. Guidelines for systematic review in conservation and environmental management. Conserv Biol. 2006;20:1647–56. James KL, Randall NP, Haddaway NR. A methodology for systematic mapping in environmental sciences. Environ Evid. 2016;5:7. https://doi.org/10.1186/s13750-016-0059-6. Minozzi S, Cinquini M, Gianola S, Gonzalez-Lorenzo M, Banzi R. The revised Cochrane risk of bias tool for randomized trials (RoB 2) showed low interrater reliability and challenges in its application. J Clin Epidemiol. 2020;126:37–44. Minozzi S, Cinquini M, Gianola S, Castellini G, Gerardi C, Banzi R. Risk of bias in nonrandomized studies of interventions showed low inter-rater reliability and challenges in its application. J Clin Epidemiol. 2019;112:28–34.