On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models

Biometrics

Volumn 73, Issue 4, 2017, Pages 1379-1387

  Tang, Yongqiang ^a  

^a SHIRE   (United States)

Author keywords

Control based pattern mixture model; Delta adjusted imputation; Missing not at random; Mixed effects model for repeated measures; Rubin's variance estimator; Uncongeniality

Indexed keywords

MAXIMUM LIKELIHOOD ESTIMATION;

CONTROL-BASED PATTERN MIXTURE MODEL; DELTA-ADJUSTED IMPUTATION; MISSING NOT AT RANDOM; MIXED EFFECT MODEL FOR REPEATED MEASURE; MIXED EFFECTS MODELS; MIXTURE MODELING; REPEATED MEASURES; RUBIN VARIANCE ESTIMATOR; UNCONGENIALITY; VARIANCE ESTIMATORS;

SENSITIVITY ANALYSIS;

BOOTSTRAPPING; EXPERIMENTAL STUDY; MAXIMUM LIKELIHOOD ANALYSIS; MODEL VALIDATION; NUMERICAL MODEL; SIMULATION;

ANTIDEPRESSANT AGENT;

CLINICAL TRIAL (TOPIC); COMPUTER SIMULATION; HUMAN; STATISTICAL BIAS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

ANTIDEPRESSIVE AGENTS; BIAS; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL;

EID: 85014697386     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12702     Document Type: Article

References (28)

1. Carpenter, J. R., Roger, J. H., and 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.
2. Carpenter, J. R., Roger, J. H., Cro, S., and Kenward, M. G. (2014). Response to comments by Seaman et al. on analysis of longitudinal trials with protocol deviation: A framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics 24, 1363–1369.
3. CHMP (2010). EMA Guideline on Missing data in Confirmatory Clinical Trials (EMA/CPMP/EWP/1776/99). London: CHAMP.
4. Daniels, M. J. and Hogan, J. W. (2008). Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Boca Raton, FL: Chapman & Hall/CRC.
5. Hughes, R. A., Sterne, J. A. C., and Tilling, K. (2016). Comparison of imputation variance estimators. Statistical Methods in Medical Research 25, 2541–2557.
6. ICH E9 Steering Committee (2014). E9(R1): Addendum to statistical principles for clinical trials on choosing appropriate estimands and defining sensitivity analyses in clinical trials.
7. Kenward, M. G. and Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997.
8. Kim, J. K. (2004). Finite sample properties of multiple imputation estimator. The Annals of Statistics 32, 766–783.
9. Kim, J. K., Brick, J. M., Fuller, W. A., and Kalton, G. (2006). On the bias of the multiple-imputation variance estimator in survey sampling. Journal of the Royal Statistical Society, Series B 68, 509–521.
10. Little, R. and Yau, L. (1996). Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52, 1324–1333.
11. Liu, G. F. and Pang, L. (2016). On analysis of longitudinal clinical trials with missing data using reference-based imputation. Journal of Biopharmaceutical Statistics 26, 924–936.
12. Lu, K. (2014a). An analytic method for the placebo-based pattern-mixture model. Statistics in Medicine 33, 1134–1145.
13. Lu, K. (2014b). Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis. Statistical Methods in Medical Research https://doi.org/10.1177/0962280214554439
14. 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.
15. Meng, X. (1994). Multiple-imputation inference with uncongenial sources of input. Statistical Science 9, 538–573.
16. Meng, X. L. and Romero, M. (2003). Discussion: Efficiency and self-efficiency with multiple imputation inference. International Statistical Review 71, 607–618.
17. National Research Council (2010). The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press.
18. Robins, J. M. and Wang, N. (2000). Inference for imputation estimators. Biometrika 87, 113–124.
19. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley Sons, Inc.
20. Seaman, S. R., White, I. R., and Leacy, F. P. (2014). Comment on analysis of longitudinal trials with protocol deviations: A framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics 24, 1358–1362.
21. Siddiqui, O. (2011). MMRM versus MI in dealing with missing data comparison based on 25 NDA data sets. Journal of Biopharmaceutical Statistics 21, 423–436.
22. Siddiqui, O., Hung, J. H. M., and O'Neill, R. (2009). MMRM vs. LOCF: A comprehensive comparison based on simulation study and 25 NDA datasets. Journal of Biopharmaceutical Statistics 19, 227–246.
23. Tang, Y. (2015). Short notes on maximum likelihood inference for control-based pattern-mixture models. Pharmaceutical Statistics 14, 395–399.
24. Tang, Y. (2016). An efficient monotone data augmentation algorithm for multiple imputation in a class of pattern mixture models. Journal of Biopharmaceutical Statistics https://doi.org/10.1080/10543406.2016.1167075
25. Tang, Y. (2017a). Closed-form REML estimators and sample size determination for mixed effects models for repeated measures under monotone missingness. Statistics in Medicine https://doi.org/10.1002/sim.7270
26. Tang, Y. (2017b). An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS. Statistics in Biopharmaceutical Research 9, 116–125.
27. Thijs, H., Molenberghs, G., Michiels, B., Verbeke, G., and Curran, D. (2002). Strategies to fit pattern-mixture models. Biostatistics 3, 245–265.
28. Wang, C. and Daniels, M. (2011). A note on MAR, identifying restrictions, and sensitivity analysis in pattern mixture models with and without covariates for incomplete data. Biometrics 67, 810–818.