Comment on "analysis of longitudinal trials with protocol deviations: a framework for relevant, accessible assumptions, and inference via multiple imputation," by Carpenter, Roger, and Kenward
- PMID: 24915418
- PMCID: PMC4241629
- DOI: 10.1080/10543406.2014.928306
Comment on "analysis of longitudinal trials with protocol deviations: a framework for relevant, accessible assumptions, and inference via multiple imputation," by Carpenter, Roger, and Kenward
Comment in
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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 23:1352-1371.J Biopharm Stat. 2014;24(6):1363-9. doi: 10.1080/10543406.2014.960085. J Biopharm Stat. 2014. PMID: 25215553 No abstract available.
Comment on
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Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.J Biopharm Stat. 2013;23(6):1352-71. doi: 10.1080/10543406.2013.834911. J Biopharm Stat. 2013. PMID: 24138436
References
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- Carpenter J. R., Roger J. H., Kenward M. G. Analysis of longitudinal trials with protocol deviations: A framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics. 2013;23:1352–1371. - PubMed
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- Lu K. An analytic method for the placebo-based pattern-mixture model. Statistics in Medicine. 2014;33:1134–1145. - PubMed
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- Meng X. L. Multiple-imputation inferences with uncongenial sources of input. Statistical Science. 1994;9:538–558.
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- Robins J. M., Wang N. Inference for imputation estimators. Biometrika. 2000;87:113–124.
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- Schafer J. L. 2012 http://CRAN.R-project.org/package=norm norm: Analysis of Multivariate Normal Datasets with Missing Values. R Package Version 1.0–9.4 (Ported to R by Alvaro A. Novo)
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