An Efficient Multiple Imputation Algorithm for Control-Based and Delta-Adjusted Pattern Mixture Models using SAS

Statistics in Biopharmaceutical Research

Volumn 9, Issue 1, 2017, Pages 116-125

  Tang, Yongqiang ^a  

^a SHIRE   (United States)

Author keywords

Control based imputation; Delta adjusted imputation; Missing not at random; Mixed effects model for repeated measures

Indexed keywords

ALGORITHM; ARTICLE; INFORMATION PROCESSING; MIXED EFFECT MODELS FOR REPEATED MEASURE; PARAMETERS; PATTERN MIXTURE MODEL; PROPORTIONAL HAZARDS MODEL; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION;

EID: 85014604871     PISSN: None     EISSN: 19466315     Source Type: Journal    
DOI: 10.1080/19466315.2016.1225595     Document Type: Article

References (21)

1. Ayele, B. T., Lipkovich, I., Molenberghs, G., and Mallinckrodt, C. H., (2014), “A Multiple-Imputation-Based Approach to Sensitivity Analyses and Effectiveness Assessments in Longitudinal Clinical Trials,” Journal of Biopharmaceutical Statistics, 24, 211–228.
2. Carpenter, J., Roger, J., and Kenward, M., (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.
3. CHMP (2010), EMA Guideline on Missing Data in Confirmatory Clinical Trials (EMA/CPMP/EWP/1776/99), London:CHAMP.
4. ICH E9 (1999), “Statistical Principles for Clinical Trials:ICH Harmonized Tripartite Guideline,” Statistics in Medicine, 18, 1905–1942.
5. Little, R., and Yau, L., (1996), “Intent-to-Treat Analysis for Longitudinal Studies with Drop-outs,” Biometrics, 52, 1324–1333.
6. Liu, J. S., (1994), “The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem,” Journal of the American Statistical Association, 89, 958–966.
7. Lu, K., (2014a), “An Analytic Method for the Placebo-Based Pattern-mixture Model,” Statistics in Medicine, 33, 1134–1145.
8. ——— (2014b), “An Extension of the Placebo-Based Pattern-Mixture Model,” Pharmaceutical Statistics, 13, 103–109.
9. Mallinckrodt, C., Roger, J., Chuang-stein, C., Molenberghs, G., Lane, P. W., O’kelly, M., Ratitch, B., Xu, L., Gilbert, S., Mehrotrak, D. V., Wolfinger, R., and Thijs, H., (2013), “Missing Data:Turning Guidance Into Action,” Statistics in Biopharmaceutical Research, 5, 369–382.
10. Meng, X., (1994), “Multiple-Imputation Inference with Uncongenial Sources of Input,” Statistical Science, 9, 538–573.
11. National Research Council (2010), The Prevention and Treatment of Missing Data in Clinical Trials, Washington, DC:The National Academies Press.
12. Phillips, A., Abellan-Andres, J., Soren, A., Bretz, F., Fletcher, C., France, L., Garrett, A., Harris, R., Kjaer, M., Keene, O., Morgan, D., and James Roger, M. O., (2016), “Estimands:Discussion Points from the psi Estimands and Sensitivity Expert Group,” Pharmaceutical Statistics, DOI:10.1002/pst.1745.
13. Ratitch, B., and O’Kelly, M., (2011), “Implementation of Pattern-mixture Models Using Standard SAS/STAT Procedures,” in Proceedings of PharmaSUG 2011 (Pharmaceutical Industry SAS Users Group), SP04, Nashville, TN.
14. Ratitch, B., O’Kelly, M., and Tosiello, R., (2013), “Missing Data in Clinical Trials:from Clinical Assumptions to Statistical Analysis Using Pattern Mixture Models,” Pharmaceutical Statistics, 12, 337–347.
15. Rubin, D., (1987), Multiple Imputation for Nonresponse in Surveys, New York:Wiley.
16. Schafer, J. L., (1997), Analysis of Incomplete Multivariate Data, London:Chapman Hall.
17. 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.
18. Tang, Y., (2015), “An Efficient Monotone data Augmentation Algorithm for Bayesian Analysis of Incomplete Longitudinal Data,” Statistics and Probability Letters, 104, 146–152.
19. ——— (2016), “An Efficient Monotone data Augmentation Algorithm for Multiple Imputation in a Class of Pattern Mixture Models,” Journal of Biopharmaceutical Statistics, DOI:10.1080/10543406.2016.1167075.
20. ——— (2017), “On the Multiple Imputation Variance Estimator for Control-Based and Delta-Adjusted Pattern Mixture Models,” Biometrics, submitted.
21. van Buuren, S., (2007), “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification,” Statistical Methods in Medical Research, 16, 219–242.