Useful references

Mistakes and misunderstandings tend to repeat themselves. Here is a limited collection of references to publications collected during statistical reviewing. They are surprisingly helpful when performing critical reviews, explaining problems, and motivating revisions or reanalyses. The references are divided into the following categories:

  • Estimators
  • ICH
  • ICMJE
  • Meta-analysis
  • Miscellaneous
  • Observational studies
  • Prediction/AI
  • Randomised trials
  • Reporting
  • Statistical inference
  • Study design
  • Terminology

Estimators

1.
Austin PC, Ibrahim M, Putter H. Accounting for Competing Risks in Clinical Research. JAMA [Internet]. 2024 Jun 25 [cited 2024 Jun 29];331(24):2125–6. Available from: https://doi.org/10.1001/jama.2024.4970
1.
Phillips A, Clark T. Estimands in practice: Bridging the gap between study objectives and statistical analysis. Pharm Stat. 2021 Jan;20(1):68–76.
1.
Spronk I, Korevaar JC, Poos R, Davids R, Hilderink H, Schellevis FG, et al. Calculating incidence rates and prevalence proportions: not as simple as it seems. BMC Public Health [Internet]. 2019 May 6 [cited 2021 Dec 31];19(1):512. Available from: https://doi.org/10.1186/s12889-019-6820-3
1.
Sayers A, Evans JT, Whitehouse MR, Blom AW. Are competing risks models appropriate to describe implant failure? Acta Orthop. 2018 Jun;89(3):256–8.
1.
Schober P, Boer C, Schwarte LA. Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia [Internet]. 2018 May [cited 2022 May 9];126(5):1763–8. Available from: https://journals.lww.com/anesthesia-analgesia/Fulltext/2018/05000/Correlation_Coefficients__Appropriate_Use_and.50.aspx
1.
Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Measures of agreement. Perspect Clin Res [Internet]. 2017 [cited 2022 May 9];8(4):187–91. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654219/
1.
Xie W, Department of Epidemiology  and Biostatistics ICL, Zheng F. Robust Cox Regression as an Alternative Method to Estimate Adjusted Relative Risk in Prospective Studies with Common Outcomes. International Journal of Statistics in Medical Research [Internet]. 2016 [cited 2023 Mar 25];5(4):231. Available from: https://www.academia.edu/69617712/Robust_Cox_Regression_as_an_Alternative_Method_to_Estimate_Adjusted_Relative_Risk_in_Prospective_Studies_with_Common_Outcomes
1.
Barnett AG. Regression to the mean: what it is and how to deal with it. International Journal of Epidemiology [Internet]. 2004 Aug 27 [cited 2023 Jun 22];34(1):215–20. Available from: https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dyh299
1.
Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology [Internet]. 2003 Oct 20 [cited 2021 Dec 31];3(1):21. Available from: https://doi.org/10.1186/1471-2288-3-21
1.
McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003 May 15;157(10):940–3.
1.
Campbell MK, Torgerson DJ. Bootstrapping: estimating confidence intervals for cost-effectiveness ratios. QJM: An International Journal of Medicine [Internet]. 1999 Mar 1 [cited 2023 Oct 5];92(3):177–82. Available from: https://doi.org/10.1093/qjmed/92.3.177
1.
Altman DG. Confidence intervals for the number needed to treat. BMJ. 1998 Nov 7;317(7168):1309–12.
1.
Davies HT, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ. 1998 Mar 28;316(7136):989–91.
1.
What’s the Relative Risk? A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes | Research, Methods, Statistics | JAMA | JAMA Network [Internet]. [cited 2021 Dec 31]. Available from: https://jamanetwork.com/journals/jama/fullarticle/188182

ICH

1.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ [Internet]. 2021 Mar 29 [cited 2022 Jun 9];372:n71. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005924/
1.
Evans S. When and How Can Endpoints Be Changed after Initiation of a Randomized Clinical Trial? PLoS Clin Trials [Internet]. 2007 Apr 13 [cited 2021 Dec 31];2(4):e18. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852589/
1.
Reflection Paper on Methodological Issues in Confirmatory Clinical Trials Planned with an Adaptive Design. 2007;10.
1.
Committee for medicinal products for human use (CHMP) guideline on the choice of the non-inferiority margin. Statist Med [Internet]. 2006 May 30 [cited 2021 Dec 31];25(10):1628–38. Available from: https://onlinelibrary.wiley.com/doi/10.1002/sim.2584
1.
Committee for medicinal products for human use (CHMP) guideline on Data Monitoring Committees. Statist Med [Internet]. 2006 May 30 [cited 2022 Jan 25];25(10):1639–45. Available from: https://onlinelibrary.wiley.com/doi/10.1002/sim.2585
1.
Watanabe H, Takahashi K. Points to Consider on Multiplicity Issues in Clinical Trials. JJB [Internet]. 2006 [cited 2021 Dec 31];27(Special_Issue):S73–7. Available from: http://www.jstage.jst.go.jp/article/jjb/27/Special_Issue/27_Special_Issue_S73/_article/-char/ja/
1.
E 9 Statistical Principles for Clinical Trials. 2006;37.
1.
E 10 Choice of Control Group in Clinical Trials. 2006;30.
1.
E 3 Structure and Content of Clinical Study Reports. 2006;48.
1.
Committee for Proprietary Medicinal Products (CPMP). Points to consider on switching between superiority and non-inferiority: Points to consider on switching between superiority and non-inferiority. British Journal of Clinical Pharmacology [Internet]. 2001 [cited 2021 Dec 31];52(3):223–8. Available from: http://doi.wiley.com/10.1046/j.1365-2125.2001.01397-3.x
1.
Concept paper on extrapolation of efficacy and safety in medicine development. :7.
1.
Guideline on the investigation of subgroups in confirmatory clinical trials. :20.
1.
Guideline on adjustment for baseline covariates in clinical trials. :11.

ICMJE

  1. ICMJE. Preparing a Manuscript for Submission to a Medical Journal [Internet]. Available from: https://icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html
  2. ICMJE. Up-Dated ICMJE Recommendations (January 2024) [Internet]. Available from: https://icmje.org/news-and-editorials/updated_recommendations_jan2024.html

Meta-analysis

1.
Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Medical Research Methodology [Internet]. 2021 Jun 26 [cited 2022 Feb 17];21(1):134. Available from: https://doi.org/10.1186/s12874-021-01306-w
1.
Spineli LM, Pandis N. Prediction interval in random-effects meta-analysis. American Journal of Orthodontics and Dentofacial Orthopedics [Internet]. 2020 Apr 1 [cited 2024 Aug 14];157(4):586–8. Available from: https://www.ajodo.org/article/S0889-5406(20)30001-9/fulltext
1.
Potter GE. Dismantling the Fragility Index: A demonstration of statistical reasoning. Statistics in Medicine [Internet]. 2020 [cited 2022 Jul 28];39(26):3720–31. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8689
1.
Lin L, Xu C. Arcsine-based transformations for meta-analysis of proportions: Pros, cons, and alternatives. Health Science Reports [Internet]. 2020 [cited 2022 Jan 24];3(3):e178. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/hsr2.178
1.
Seide SE, Röver C, Friede T. Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies. BMC Medical Research Methodology [Internet]. 2019 Jan 11 [cited 2022 Mar 10];19(1):16. Available from: https://doi.org/10.1186/s12874-018-0618-3
1.
Chaimani A, Caldwell DM, Li T, Higgins JP, Salanti G. Undertaking network meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions [Internet]. John Wiley & Sons, Ltd; 2019 [cited 2022 Jul 25]. p. 285–320. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119536604.ch11
1.
Fusar-Poli P, Radua J. Ten simple rules for conducting umbrella reviews. Evid Based Mental Health [Internet]. 2018 [cited 2023 Feb 14];21(3):95–100. Available from: https://ebmh.bmj.com/lookup/doi/10.1136/ebmental-2018-300014
1.
Faber T, Ravaud P, Riveros C, Perrodeau E, Dechartres A. Meta-analyses including non-randomized studies of therapeutic interventions: a methodological review. BMC Med Res Methodol [Internet]. 2016 Mar 22 [cited 2021 Dec 31];16:35. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804609/
1.
IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open [Internet]. 2016 [cited 2023 Jul 13];6(7):e010247. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2015-010247
1.
Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods [Internet]. 2012 [cited 2022 Jul 25];3(2):80–97. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jrsm.1037
1.
Higgins JPT, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc [Internet]. 2009 Jan [cited 2021 Dec 31];172(1):137–59. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667312/
1.
Rücker G, Schwarzer G, Carpenter JR, Schumacher M. Undue reliance on I2 in assessing heterogeneity may mislead. BMC Medical Research Methodology [Internet]. 2008 Nov 27 [cited 2024 May 31];8(1):79. Available from: https://doi.org/10.1186/1471-2288-8-79
1.
Bender R, Bunce C, Clarke M, Gates S, Lange S, Pace NL, et al. Attention should be given to multiplicity issues in systematic reviews. Journal of Clinical Epidemiology [Internet]. 2008 Sep 1 [cited 2024 May 9];61(9):857–65. Available from: https://www.jclinepi.com/article/S0895-4356(08)00093-0/abstract
1.
Stewart LA, Tierney JF. To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof. 2002 Mar;25(1):76–97.
1.
Chapter 11: Undertaking network meta-analyses [Internet]. [cited 2022 Jul 25]. Available from: https://training.cochrane.org/handbook/current/chapter-11

Miscellaneous

1.
Haslberger M, Gestrich S, Strech D. Reporting of retrospective registration in clinical trial publications: a cross-sectional study of German trials. BMJ Open [Internet]. 2023 Apr 1 [cited 2023 Sep 11];13(4):e069553. Available from: https://bmjopen.bmj.com/content/13/4/e069553
1.
Cashin AG, Richards GC, DeVito NJ, Mellor DT, Lee H. Registration of health and medical research. BMJ Evidence-Based Medicine [Internet]. 2021 Dec 21 [cited 2022 Jan 20]; Available from: https://ebm.bmj.com/content/early/2021/12/21/bmjebm-2021-111836
1.
Zhang Y, Hedo R, Rivera A, Rull R, Richardson S, Tu XM. Post hoc power analysis: is it an informative and meaningful analysis? Gen Psychiatr [Internet]. 2019 Aug 8 [cited 2023 Jun 27];32(4):e100069. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738696/
1.
Bellomo R. The dangers of dogma in medicine. Medical Journal of Australia [Internet]. 2011 [cited 2023 Feb 1];195(7):372–3. Available from: https://onlinelibrary.wiley.com/doi/abs/10.5694/mja11.10866
1.
Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007 Mar 15;165(6):710–8.
1.
Ruxton GD. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behavioral Ecology [Internet]. 2006 Jul 1 [cited 2024 Aug 30];17(4):688–90. Available from: http://academic.oup.com/beheco/article/17/4/688/215960/The-unequal-variance-ttest-is-an-underused
1.
Hart A. Mann-Whitney test is not just a test of medians: differences in spread can be important. BMJ [Internet]. 2001 Aug 18 [cited 2024 Jun 26];323(7309):391–3. Available from: https://www.bmj.com/content/323/7309/391
1.
Goldstein H, Spiegelhalter DJ. League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance. Journal of the Royal Statistical Society Series A (Statistics in Society) [Internet]. 1996 [cited 2023 Jan 8];159(3):385. Available from: https://www.jstor.org/stable/10.2307/2983325?origin=crossref
1.
Soffer A. Can You Believe What You Read in Medical Journals? Chest [Internet]. 1992 [cited 2023 Feb 1];101(5):1417–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0012369216341538
1.
van Smeden M. A Very Short List of Common Pitfalls in Research Design, Data Analysis, and Reporting.
1.
Senn, Stephen. Regression to the mean.

Observational studies

1.
Ramspek CL, Steyerberg EW, Riley RD, Rosendaal FR, Dekkers OM, Dekker FW, et al. Prediction or causality? A scoping review of their conflation within current observational research. Eur J Epidemiol. 2021 Sep;36(9):889–98.
1.
Bowden J. Realising the full potential of MR-PHeWAS in cancer. Br J Cancer [Internet]. 2021 Feb 2 [cited 2022 May 26];124(3):529–30. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851387/
1.
King G, Nielsen R. Why Propensity Scores Should Not Be Used for Matching. Political Analysis [Internet]. 2019 Oct [cited 2021 Dec 31];27(4):435–54. Available from: https://www.cambridge.org/core/journals/political-analysis/article/abs/why-propensity-scores-should-not-be-used-for-matching/94DDE7ED8E2A796B693096EB714BE68B
1.
Clarke KA, Kenkel B, Rueda MR. Omitted Variables, Countervailing Effects, and the Possibility of Overadjustment. Political Science Research and Methods [Internet]. 2018 [cited 2021 Dec 31];6(2):343–54. Available from: https://ideas.repec.org/a/cup/pscirm/v6y2018i02p343-354_00.html
1.
Hanley JA, Foster BJ. Avoiding blunders involving “immortal time.” Int J Epidemiol. 2014 Jun;43(3):949–61.
1.
Joseph KS, Mehrabadi A, Lisonkova S. Confounding by Indication and Related Concepts. Curr Epidemiol Rep [Internet]. 2014 Mar 1 [cited 2021 Dec 31];1(1):1–8. Available from: https://doi.org/10.1007/s40471-013-0004-y
1.
Okoli GN, Sanders RD, Myles P. Demystifying propensity scores. Br J Anaesth [Internet]. 2014 Jan [cited 2024 Sep 17];112(1):13–5. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3854550/
1.
Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013 Feb 15;177(4):292–8.
1.
Shmueli G. To Explain or to Predict? Statist Sci [Internet]. 2010 Aug 1 [cited 2023 Sep 15];25(3). Available from: https://projecteuclid.org/journals/statistical-science/volume-25/issue-3/To-Explain-or-to-Predict/10.1214/10-STS330.full
1.
Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010 Apr;39(2):417–20.
1.
Schisterman EF, Cole SR, Platt RW. Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies. Epidemiology [Internet]. 2009 Jul [cited 2022 May 26];20(4):488–95. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2744485/
1.
Sjölander A. Propensity scores and M-structures. Stat Med. 2009 Apr 30;28(9):1416–20; author reply 1420-1423.
1.
Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Medical Research Methodology [Internet]. 2008 Oct 30 [cited 2022 Mar 8];8(1):70. Available from: https://doi.org/10.1186/1471-2288-8-70
1.
Greenland S. Invited Commentary: Variable Selection versus Shrinkage in the Control of Multiple Confounders. American Journal of Epidemiology [Internet]. 2008 Mar 1 [cited 2022 May 25];167(5):523–9. Available from: https://doi.org/10.1093/aje/kwm355
1.
Weiss NS, Rossing MA. Healthy Screenee Bias in Epidemiologic Studies of Cancer Incidence. Epidemiology [Internet]. 1996 [cited 2024 Jul 31];7(3):319–22. Available from: https://www.jstor.org/stable/3702872
1.
Greenland S, Schlesselman JJ, Criqui MH. The fallacy of employing standardized regression coefficients and correlations as measures of effect. Am J Epidemiol. 1986 Feb;123(2):203–8.
1.
Statistical issues in assessing hospital performance. :70.
1.
Ignoring the matching variables in cohort studies – when is it valid and why? – Sjölander – 2013 – Statistics in Medicine – Wiley Online Library [Internet]. [cited 2021 Dec 31]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5879

Prediction/AI

1.
Watson V, Smith CT, Bonnett LJ. Systematic review of methods used in prediction models with recurrent event data. Diagn Progn Res [Internet]. 2024 Aug 6 [cited 2024 Aug 8];8:13. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11302841/
1.
Hond A de, Leeuwenberg T, Bartels R, Buchem M van, Kant I, Moons KG, et al. From text to treatment: the crucial role of validation for generative large language models in health care. The Lancet Digital Health [Internet]. 2024 Jul 1 [cited 2024 Jun 23];6(7):e441–3. Available from: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00111-0/fulltext?dgcid=raven_jbs_etoc_email
1.
Hicks MT, Humphries J, Slater J. ChatGPT is bullshit. Ethics Inf Technol [Internet]. 2024 Jun 8 [cited 2024 Jun 21];26(2):38. Available from: https://doi.org/10.1007/s10676-024-09775-5
1.
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ [Internet]. 2024 Apr 16 [cited 2024 May 31];e078378. Available from: https://www.bmj.com/lookup/doi/10.1136/bmj-2023-078378
1.
Riley RD, Pate A, Dhiman P, Archer L, Martin GP, Collins GS. Clinical prediction models and the multiverse of madness. BMC Med [Internet]. 2023 Dec 18 [cited 2023 Dec 20];21(1):502. Available from: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-023-03212-y
1.
Mandel F, Ghosh RP, Barnett I. Neural Networks for Clustered and Longitudinal Data Using Mixed Effects Models. Biometrics [Internet]. 2023 Jun 1 [cited 2024 Jun 23];79(2):711–21. Available from: https://doi.org/10.1111/biom.13615
1.
Van Calster B, Steyerberg EW, Wynants L, van Smeden M. There is no such thing as a validated prediction model. BMC Med. 2023 Feb 24;21(1):70.
1.
Debray TPA, Collins GS, Riley RD, Snell KIE, Calster BV, Reitsma JB, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ [Internet]. 2023 Feb 7 [cited 2024 Jul 18];380:e071058. Available from: https://www.bmj.com/content/380/bmj-2022-071058
1.
de Hond AAH, Leeuwenberg AM, Hooft L, Kant IMJ, Nijman SWJ, van Os HJA, et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. npj Digit Med [Internet]. 2022 Jan 10 [cited 2024 Jul 18];5(1):1–13. Available from: https://www.nature.com/articles/s41746-021-00549-7
1.
Ramspek CL, Steyerberg EW, Riley RD, Rosendaal FR, Dekkers OM, Dekker FW, et al. Prediction or causality? A scoping review of their conflation within current observational research. Eur J Epidemiol. 2021 Sep;36(9):889–98.
1.
Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clinical Kidney Journal [Internet]. 2021 Feb 3 [cited 2023 Mar 22];14(1):49–58. Available from: https://academic.oup.com/ckj/article/14/1/49/6000246
1.
Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW, Bossuyt P, et al. Calibration: the Achilles heel of predictive analytics. BMC Medicine [Internet]. 2019 Dec 16 [cited 2021 Dec 31];17(1):230. Available from: https://doi.org/10.1186/s12916-019-1466-7
1.
Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016 Jan;69:245–7.
1.
Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Medical Research Methodology [Internet]. 2014 Mar 19 [cited 2022 Aug 4];14(1):40. Available from: https://doi.org/10.1186/1471-2288-14-40
1.
Bouwmeester W, Twisk JW, Kappen TH, van Klei WA, Moons KG, Vergouwe Y. Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Medical Research Methodology [Internet]. 2013 Feb 15 [cited 2021 Dec 31];13(1):19. Available from: https://doi.org/10.1186/1471-2288-13-19
1.
Cawley GC, Talbot NLC. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. :29.

Randomised trials

1.
Billot L, Copas A, Leyrat C, Forbes A, Turner EL. How should a cluster randomized trial be analyzed? Journal of Epidemiology and Population Health [Internet]. 2024 Feb 1 [cited 2024 Aug 22];72(1):202196. Available from: https://www.sciencedirect.com/science/article/pii/S2950433324000077
1.
Ahn E, Kang H. Intention-to-treat versus as-treated versus per-protocol approaches to analysis. Korean J Anesthesiol [Internet]. 2023 Dec [cited 2024 Oct 11];76(6):531–9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10718629/
1.
Haslberger M, Gestrich S, Strech D. Reporting of retrospective registration in clinical trial publications: a cross-sectional study of German trials. BMJ Open [Internet]. 2023 Apr 1 [cited 2023 Sep 11];13(4):e069553. Available from: https://bmjopen.bmj.com/content/13/4/e069553
1.
Phillips R, Cro S, Wheeler G, Bond S, Morris TP, Creanor S, et al. Visualising harms in publications of randomised controlled trials: consensus and recommendations. BMJ [Internet]. 2022 May 16 [cited 2022 May 17];377:e068983. Available from: https://www.bmj.com/content/377/bmj-2021-068983
1.
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med [Internet]. 2020 [cited 2022 Jun 8];26(9):1364–74. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598943/
1.
Mauri L, D’Agostino RB. Challenges in the Design and Interpretation of Noninferiority Trials. New England Journal of Medicine [Internet]. 2017 Oct 5 [cited 2023 Oct 10];377(14):1357–67. Available from: https://doi.org/10.1056/NEJMra1510063
1.
Li G, Taljaard M, Van den Heuvel ER, Levine MA, Cook DJ, Wells GA, et al. An introduction to multiplicity issues in clinical trials: the what, why, when and how. International Journal of Epidemiology [Internet]. 2017 Apr 1 [cited 2022 Nov 3];46(2):746–55. Available from: https://doi.org/10.1093/ije/dyw320
1.
Kahan BC, Rehal S, Cro S. Risk of selection bias in randomised trials. Trials [Internet]. 2015 Sep 10 [cited 2021 Dec 31];16(1):405. Available from: https://doi.org/10.1186/s13063-015-0920-x
1.
Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ [Internet]. 2015 Feb 6 [cited 2023 Sep 27];350(feb06 1):h391–h391. Available from: https://www.bmj.com/lookup/doi/10.1136/bmj.h391
1.
Knol MJ, Groenwold RHH, Grobbee DE. P-values in baseline tables of randomised controlled trials are inappropriate but still common in high impact journals. Eur J Prev Cardiol. 2012 Apr;19(2):231–2.
1.
Hackshaw A, Kirkwood A. Interpreting and reporting clinical trials with results of borderline significance. BMJ [Internet]. 2011 Jul 4 [cited 2022 Aug 12];343:d3340. Available from: https://www.bmj.com/content/343/bmj.d3340
1.
Tangri N, Kitsios GD, Su SH, Kent DM. Accounting for center effects in multicenter trials. Epidemiology. 2010 Nov;21(6):912–3.
1.
Moher D, Hopewell S, Schulz KF, Montori V, Gotzsche PC, Devereaux PJ, et al. CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ [Internet]. 2010 Mar 23 [cited 2022 Oct 6];340(mar23 1):c869–c869. Available from: https://www.bmj.com/lookup/doi/10.1136/bmj.c869
1.
Evans S. When and How Can Endpoints Be Changed after Initiation of a Randomized Clinical Trial? PLoS Clin Trials [Internet]. 2007 Apr 13 [cited 2021 Dec 31];2(4):e18. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852589/
1.
Committee for medicinal products for human use (CHMP) guideline on the choice of the non-inferiority margin. Statist Med [Internet]. 2006 May 30 [cited 2021 Dec 31];25(10):1628–38. Available from: https://onlinelibrary.wiley.com/doi/10.1002/sim.2584
1.
Watanabe H, Takahashi K. Points to Consider on Multiplicity Issues in Clinical Trials. JJB [Internet]. 2006 [cited 2021 Dec 31];27(Special_Issue):S73–7. Available from: http://www.jstage.jst.go.jp/article/jjb/27/Special_Issue/27_Special_Issue_S73/_article/-char/ja/
1.
E 9 Statistical Principles for Clinical Trials. 2006;37.
1.
Schulz KF, Grimes DA. Multiplicity in randomised trials II: subgroup and interim analyses. Lancet. 2005 May 7;365(9471):1657–61.
1.
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Study design

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Terminology

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