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Research Article

Fast tipping point sensitivity analyses in clinical trials with missing continuous outcomes under multiple imputation

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Pages 942-953 | Received 04 Sep 2021, Accepted 18 Mar 2022, Published online: 02 Jun 2022

References

  • American Diabetes Association. 2021. 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes—2021. Diabetes Care 44 (Supplement_1):S111–124. doi:10.2337/dc21-S009.
  • Aroda, V. R., J. Rosenstock, Y. Terauchi, Y. Altuntas, N. M. Lalic, E. C. Morales Villegas, O. K. Jeppesen, E. Christiansen, C. L. Hertz, M. Haluzík, et al. 2019. PIONEER 1: randomized clinical trial of the efficacy and safety of oral semaglutide monotherapy in comparison with placebo in patients with type 2 diabetes. Diabetes Care. 42 (9):1724–1732. doi:10.2337/dc19-0749.
  • Cro, S., T. P. Morris, M. G. Kenward, and J. R. Carpenter. 2020. Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide. Statistics in Medicine 39 (21):2815–2842. doi:10.1002/sim.8569.
  • International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (2019). ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials. Accessed 05 August 2021. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf
  • Kreinovich, V., H. T. Nguyen, and S. Niwitpong. 2008. Statistical hypothesis testing under interval uncertainty: an overview. Journal of Intelligent Technologies and Applied Statistics 1:1–33.
  • Kreinovich, V., G. Xiang, S. A. Starks, L. Longpre, M. Ceberio, R. Araiza, J. Beck, R. Kandathi, A. Nayak, R. Torres, et al. 2006. Towards Combining Probabilistic and Interval Uncertainty in Engineering Calculations: Algorithms for Computing Statistics under Interval Uncertainty, and Their Computational Complexity. Reliable Computing 12:471–501.
  • Krŭzik, M. 2000. Bauer’s maximum principle and hulls of sets. Calculus of Variations and Partial Differential Equations 11:319–327.
  • Landsmann, Z. 2008. Minimization of the root of a quadratic functional under an affine equality constraint. Journal of Computational and Applied Mathematics 216 (2):319–327. doi:10.1016/j.cam.2007.05.010.
  • Lipkovich, I., B. Ratitch, and M. O’Kelly. 2016. Sensitivity to censored-at-random assumption in the analysis of time-to-event endpoints. Pharmaceutical Statistics 16 (3):216–229. doi:10.1002/pst.1738.
  • Liu, G. F., and L. Pang. 2017. Control-based imputation and delta-adjustment stress test for missing data analysis in longitudinal clinical trials. Statistics in Biopharmaceutical Research 9 (2):186–194. doi:10.1080/19466315.2016.1256830.
  • Liublinska, V., and D. B. Rubin. 2014. Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial. Statistics in Medicine 33 (24):4170–4185. doi:10.1002/sim.6197.
  • Lu, K. 2021. An alternative implementation of reference- based controlled imputation procedures . Statistics in Biopharmaceutical Research 13 (4):483–491. doi:10.1080/19466315.2020.1796781.
  • Mallinckrodt, C. H., W. Clark, and S. R. David. 2001. Accounting for dropout bias using mixed-effects models. Journal of Biopharmaceutical Statistics 11 (1–2):9–21. doi:10.1081/BIP-100104194.
  • Merothra, D. V., F. Lio, and T. Permutt. 2017. Missing data in clinical trials: Control-based mean imputation and sensitivity analysis. Pharmaceutical Statistics 16 (5):378–392. doi:10.1002/pst.1817.
  • Mitroiu, M., K. O. Rengerink, S. Teerenstra, F. Pétavy, K. C. B. Roes, M. Hide, and C. Nishigori. 2020. A narrative review of estimands in drug development and regulatory evaluation: Old wine in new barrels? Trials 21 (1):23. doi:10.1186/s13063-019-3878-2.
  • National Research Council, Panel on Handling Missing Data in Clinical Trials, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. 2010. The prevention and treatment of missing data in clinical trials. Washington (DC): National Academies Press.
  • Ouyang, J., K. J. Carroll, G. Koch, and J. Li. 2017. Coping with missing data in phase III pivotal registration trials:Tolvaptan in subjects with kidney disease, a case study. Pharmaceutical Statistics 16 (4):250–266. doi:10.1002/pst.1808.
  • Pagana, K. D., T. J. Pagana, and T. N. Pagana. 2021. Mosby’s diagnostic and laboratory test reference. 15 ed. St Louis, Missouri: Elsevier.
  • Pardalos, P. M., and J. B. Rosen. 1986. Methods for global concave minimization: A bibliographic survey. Siam Review 28 (3):367–379. doi:10.1137/1028106.
  • Permutt, T. 2016. Sensitivity analysis for missing data in regulatory submissions. Statistics in Medicine 35 (17):2876–2879. doi:10.1002/sim.6753.
  • R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Ratitch, B., M. O’Kelly, and R. Tosiello. 2013. Missing data in clinical trials: From clinical assumptions to statistical analysis using pattern mixture models. Pharmaceutical Statistics 12 (6):337–347. doi:10.1002/pst.1549.
  • Rubin, D. B. 1987. Multiple imputation for nonresponse in surveys. New York: Wiley.
  • Siddiqui, O. 2011. MMRM versus MI in dealing with missing data - a comparison based on 25 NDA data sets. Journal of Biopharmaceutical Statistics 21 (3):423–436. doi:10.1080/10543401003777995.
  • Torres, C. 2019. A tipping point method to evaluate sensitivity to potential violations in missing data assumptions. ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, Washington (DC).
  • Tsiatis, A., M. Davidian, M. Zhang, and X. Lu. 2008. Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach. Statistics in Medicine 27 (23):4658–4677. doi:10.1002/sim.3113.
  • White, I. R., J. Carpenter, and N. J. Horton. 2018. A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials. Statistica Sinica 28 (4):1985–2003. doi:10.5705/ss.202016.0308.
  • White, I., R. Joseph, and N. Best. 2020. A causal modelling framework for reference-based imputation and tipping point analysis in clinical trials with quantitative outcome. Journal of Biopharmaceutical Statistics 30 (2):334–350. doi:10.1080/10543406.2019.1684308.
  • Yan, X., S. Lee, and N. Li. 2009. Missing data handling methods in medical device clinical trials. Journal of Biopharmaceutical Statistics 19 (6):1085–1098. doi:10.1080/10543400903243009.

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