Evidence in Medicine. Iain K. Crombie

trials in rheumatoid arthritis: the reporting and handling of missing data in composite outcomes. Trials https://doi.org/10.1186/s13063‐016‐1402‐5.

      49 49. Miller, B.M. and Brennan, L. (2015). Measuring and reporting attrition from obesity treatment programs: a call to action! Obes. Res. Clin. Pract. 9: 187–202.

      50 50. Marciniak, T.A., Cherepanov, V., Golukhova, E. et al. (2016). Drug discontinuation and follow‐up rates in oral antithrombotic trials. JAMA Intern. Med. 176: 257–259.

      51 51. Hussain, J.A., Bland, M., Langan, D. et al. (2017). Quality of missing data reporting and handling in palliative care trials demonstrates that further development of the CONSORT statement is required: a systematic review. J. Clin. Epidemiol. 88: 81–91.

      52 52. White, I.R., Horton, N.J., Carpenter, J. et al. (2011). Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ https://doi.org/10.1136/bmj.d40.

      53 53. Joseph, R., Sim, J., Ogollah, R. et al. (2015). A systematic review finds variable use of the intention‐to‐treat principle in musculoskeletal randomized controlled trials with missing data. J. Clin. Epidemiol. 68: 15–24.

      54 54. Kahale, L.A., Diab, B., Khamis, A.M. et al. (2019). Potentially missing data are considerably more frequent than definitely missing data: a methodological survey of 638 randomized controlled trials. J. Clin. Epidemiol. 106: 18–31.

      55 55. Altman, D.G. (2009). Missing outcomes in randomized trials: addressing the dilemma. Open Med. 3: 51–53.

      56 56. Molnar, F.J., Hutton, B., and Fergusson, D. (2008). Does analysis using “last observation carried forward” introduce bias in dementia research? CMAJ 179: 751–753.

      57 57. Lachin, J.M. (2016). Fallacies of last observation carried forward analyses. Clin. Trials 13: 161–168.

      58 58. Lee, K.J. and Simpson, J.A. (2014). Introduction to multiple imputation for dealing with missing data. Respirology 19: 162–167.

      59 59. Jakobsen, J.C., Gluud, C., Wetterslev, J. et al. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts. BMC Med. Res. Methodol. https://doi.org/10.1186/s12874‐017‐0442‐1.

      60 60. Donders, A.R., van der Heijden, G.J., Stijnen, T. et al. (2006). Review: a gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59: 1087–1091.

      61 61. Montedori, A., Bonacini, M.I., Casazza, G. et al. (2011). Modified versus standard intention‐to‐treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross‐sectional study. Trials https://doi.org/10.1186/1745‐6215‐12‐58.

      62 62. Abraha, I., Cozzolino, F., Orso, M. et al. (2017). A systematic review found that deviations from intention‐to‐treat are common in randomized trials and systematic reviews. J. Clin. Epidemiol. 84: 37–46.

      63 63. Abraha, I. and Montedori, A. (2010). Modified intention to treat reporting in randomised controlled trials: systematic review. BMJ https://doi.org/10.1136/bmj.c2697.

      64 64. Abraha, I., Cherubini, A., Cozzolino, F. et al. (2015). Deviation from intention to treat analysis in randomised trials and treatment effect estimates: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.h2445.

      65 65. Dossing, A., Tarp, S., Furst, D.E. et al. (2016). Modified intention‐to‐treat analysis did not bias trial results. J. Clin. Epidemiol. 72: 66–74.

      66 66. Berger, V.W. (2017). Subjecting known facts to flawed empirical testing. J. Clin. Epidemiol. 84: 188.

      67 67. Rainville, T., Laskine, M., and Durand, M. (2019). Use of modified intention‐to‐treat analysis in studies of direct oral anticoagulants and risk of selection bias: a systematic review. BMJ Evid. Based Med. 24: 63–69.

      68 68. Farquhar, C.M., Showell, M.G., Showell, E.A.E. et al. (2017). Clinical trial registration was not an indicator for low risk of bias. J. Clin. Epidemiol. 84: 47–53.

      69 69. Trinquart, L., Dunn, A.G., and Bourgeois, F.T. (2018). Registration of published randomized trials: a systematic review and meta‐analysis. BMC Med. https://doi.org/10.1186/s12916‐018‐1168‐6.

      70 70. Odutayo, A., Emdin, C.A., Hsiao, A.J. et al. (2017). Association between trial registration and positive study findings: cross sectional study (epidemiological study of randomized trials‐ESORT). BMJ https://doi.org/10.1136/bmj.j917.

      71 71. Dechartres, A., Ravaud, P., Atal, I. et al. (2016). Association between trial registration and treatment effect estimates: a meta‐epidemiological study. BMC Med. https://doi.org/10.1186/s12916‐016‐0639‐x.

      72 72. Nuesch, E., Trelle, S., Reichenbach, S. et al. (2010). Small study effects in meta‐analyses of osteoarthritis trials: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.c3515.

      73 73. Dechartres, A., Trinquart, L., Boutron, I. et al. (2013). Influence of trial sample size on treatment effect estimates: meta‐epidemiological study. BMJ https://doi.org/10.1136/bmj.f2304.

      74 74. Papageorgiou, S.N., Antonoglou, G.N., Tsiranidou, E. et al. (2014). Bias and small‐study effects influence treatment effect estimates: a meta‐epidemiological study in oral medicine. J. Clin. Epidemiol. 67: 984–992.

      75 75. Pereira, T.V., Horwitz, R.I., and Ioannidis, J.P. (2012). Empirical evaluation of very large treatment effects of medical interventions. JAMA 308: 1676–1684.

      76 76. Wang, Z., Alahdab, F., Almasri, J. et al. (2016). Early studies reported extreme findings with large variability: a meta‐epidemiologic study in the field of endocrinology. J. Clin. Epidemiol. 72: 27–32.

      77 77. Gartlehner, G., Dobrescu, A., Evans, T.S. et al. (2016). Average effect estimates remain similar as evidence evolves from single trials to high‐quality bodies of evidence: a meta‐epidemiologic study. J. Clin. Epidemiol. 69: 16–22.

      78 78. Ioannidis, J.P. (2005). Contradicted and initially stronger effects in highly cited clinical research. JAMA 294: 218–228.

      79 79. Ingre, M. (2013). Why small low‐powered studies are worse than large high‐powered studies and how to protect against “trivial” findings in research: comment on Friston (2012). NeuroImage 81: 496–498.

      80 80. Walsh, M., Srinathan, S.K., McAuley, D.F. et al. (2014). The statistical significance of randomized controlled trial results is frequently fragile: a case for a fragility index. J. Clin. Epidemiol. 67: 622–628.

      81 81. Ridgeon, E.E., Young, P.J., Bellomo, R. et al. (2016). The fragility index in multicenter randomized controlled critical care trials. Crit. Care Med. 44: 1278–1284.

      82 82. Noel, C.W., McMullen, C., Yao, C. et al. (2018). The fragility of statistically significant findings from randomized trials in head and neck surgery. Laryngoscope 128: 2094–2100.

      83 83. Evaniew, N., Files, C., Smith, C. et al. (2015). The fragility of statistically significant findings from randomized trials in spine surgery: a systematic survey. Spine J. 15: 2188–2197.

      84 84. Mazzinari, G., Ball, L., Serpa Neto, A. et al. (2018). The fragility of statistically significant findings in randomised controlled anaesthesiology trials: systematic review of the medical literature. Br. J. Anaesth. 120: 935–941.

      85 85. Edwards, E., Wayant, C.,