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Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials

  • G. Frank LiuEmail author
  • James Kost
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Missing data handling in longitudinal clinical trials has gained considerable interest in recent years. Although a lot of research has been devoted to statistical methods for missing data, there is no universally best approach for analysis. It is often recommended to perform sensitivity analyses under different assumptions to assess the robustness of the analysis results from a clinical trial. To evaluate and implement statistical analysis models for missing data, Monte-Carlo simulations are often used. In this chapter, we present a few simulation-based approaches related to missing data issues in longitudinal clinical trials. First, a simulation-based approach is developed for generating monotone missing data under a variety of missing data mechanism, which allows users to specify the expected proportion of missing data at each longitudinal time point. Secondly, we consider a few simulation-based approaches to implement some recently proposed sensitivity analysis methods such as control-based imputation and tipping point analysis. Specifically, we apply a delta-adjustment approach to account for the potential difference in the estimated treatment effects between the mixed model (typically used as the primary model in clinical trials) and the multiple imputation model used to facilitate the tipping point analysis. We also present a Bayesian Markov chain Monte-Carlo method for control-based imputation which provides a more appropriate variance estimate than conventional multiple imputation. Computation programs for these methods are implemented and available in SAS.

Keywords

MIMultiple Imputation Copy Reference MCMC Sample Bayesian MCMC Clinical Trial Statistician 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ayele, B. T., Lipkovich, I., Molenberghs, G., & Mallinckrodt, C. H. (2014). A multiple imputation based approach to sensitivity analysis and effectiveness assessments in longitudinalm clinical trials. Journal of Biopharmaceutical Statistics, 24, 211–228. doi: 10.1080/10543406.2013.859148.
  2. Barnard, J., & Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86, 948–955. doi: 10.1093/biomet/86.4.948.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Carpenter, J. R., Roger, J. H., & Kenward, M. G. (2013). Analysis of longitudinal trials with protocol deviation: A Framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics, 23, 1352–1371. doi: 10.1080/10543406.2013.834911.
  4. European Medicines Agency. (2010). Guideline on missing data in confirmatory clinical trials. Retrieved from http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/09/WC500096793.pdf.
  5. Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: John Wiley.zbMATHGoogle Scholar
  6. Liu, G. F., & Pang, L. (2015). On analysis of longitudinal clinical trials with missing data using reference-based imputation. Journal of Biopharmaceutical Statistics. Advance online publication. http://dx.doi.org/10.1080/10543406.2015.1094810.
  7. Lu, K., Luo, X., & Chen, P.-Y. (2008). Sample size estimation for repeated measures analysis in randomized clinical trials with missing data. International Journal of Biostatistics, 4, 1–16. doi: 10.2202/1557-4679.1098.MathSciNetCrossRefGoogle Scholar
  8. Lu, K., Mehrotra, D. V., & Liu, G. F. (2009). Sample size determination for constrained longitudinal data analysis. Statistics in Medicine, 28, 679–699. doi: 10.1002/sim.3507.
  9. Lu, K. (2014). An analytic method for the placebo-based pattern mixture model. Statistics in Medicine, 33, 1134–1145.Google Scholar
  10. Mallinckrodt, C. H., Lane, P. W., Schnell, D., Peng, Y., & Mancuso, J. P. (2008). Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Information Journal, 42, 303–319. doi: 10.1177/009286150804200402.Google Scholar
  11. Mallinckrodt, C., Roger, J., Chuang-Stein, C., Molenberghs, G., Lane, P. W., O’Kelly, M., et al. (2013). Missing data: Turning guidance into action. Statistics in Biopharmaceutical Research, 5, 369–382. doi: 10.1080/19466315.2013.848822.
  12. National Academy of Sciences. (2010). The prevention and treatment of missing data in clinical trials. Panel on handling missing data in clinical trials [Prepared by by the Committee on National Statistics, National Research Council]. Washington, DC: National Academics Press. Retrieved from http://www.nap.edu/catalog/12955/the-prevention-and-treatmentof-missing-data-in-clinical-trials.
  13. O’Kelly, M., & Ratitch, B. (2014). Clinical trials with missing data: A guide for practitioners. West Sussex: Wiley. doi: 10.1002/9781118762516.CrossRefzbMATHGoogle Scholar
  14. Ratitch, B., O’Kelly, M., & Tosiello, R. (2013). Missing data in clinical trials: From clinical assumptions to statistical analysis using pattern mixture models. Pharmaceutical Statistics, 12, 337–347. doi: 10.1002/pst.1549.
  15. Rubin, D. B. (1987). Multiple imputationfor nonresponse in surveys. New York: Wiley. doi: 10.1002/9780470316696.CrossRefGoogle Scholar
  16. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall. doi: 10.1201/9781439821862.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Merck & Co. Inc.North WalesUSA

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