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

Journal of Biopharmaceutical Statistics

Volumn 24, Issue 6, 2014, Pages 1358-1362

  Seaman, Shaun R ^a   White, Ian R ^a   Leacy, Finbarr P ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF COVARIANCE; CLINICAL TRIAL (TOPIC); DATA ANALYSIS; HUMAN; MULTIPLE IMPUTATION; NORMAL DISTRIBUTION; NOTE; SENSITIVITY ANALYSIS; STATISTICAL PARAMETERS; UNCERTAINTY; VARIANCE; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 84910065891     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2014.928306     Document Type: Note

References (6)

1. Carpenter, J. R., Roger, J. H., Kenward, M. G. (2013). Analysis of longitudinal trials with protocol deviations: A framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics 23:1352-1371.
2. Lu, K. (2014). An analytic method for the placebo-based pattern-mixture model. Statistics in Medicine 33:1134-1145.
3. Meng, X. L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science 9:538-558.
4. Robins, J. M., Wang, N. (2000). Inference for imputation estimators. Biometrika 87:113-124.
5. Schafer, J. L. (2012). norm: Analysis of Multivariate Normal Datasets with Missing Values. R Package Version 1.0-9.4 (Ported to R by Alvaro A. Novo). http://CRAN.R-project.org/package=norm.
6. White, I. R. (2005). Uses and limitations of randomization-based efficacy estimators. Statistical Methods in Medical Research 14:327-347.