| Sign In to gain access to subscriptions and/or personal tools. |
Efficacy studies of malaria treatments in Africa: efficient estimation with missing indicators of failureCenter for Health Care Research and Policy, Case Western Reserve University, Cleveland, Ohio, USA, rhoderick.machekano{at}case.edu
Department of Medicine, San Francisco General Hospital, University of California, San Francisco, California, USA
Division of Biostatistics, University of California, Berkeley, California, USA The effect of missing data in causal inference problems is widely recognized. In malaria drug ef"cacy studies, it is often dif"cult to distinguish between new and old infections after treatment, resulting in indeterminate outcomes. Methods that adjust for possible bias from missing data include a variety of imputation procedures (extreme case analysis, hot-deck, single and multiple imputation), weighting methods, and likelihood based methods (data augmentation, EM procedures and their extensions). In this article, we focus our discussion on multiple imputation and two weighting procedures (the inverse probability weighted and the doubly robust (DR) extension), comparing the methods' applicability to the ef"cient estimation of malaria treatment effects. Simulation studies indicate that DR estimators are generally preferable because they offer protection to misspeci"cation of either the outcome model or the missingness model. We apply the methods to analyze malaria ef"cacy studies from Uganda.
This version was published on April
1, 2008 Statistical Methods in Medical Research, Vol. 17, No. 2,
191-206 (2008) |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 |
|
|
 |
Sign In to gain access to subscriptions and/or personal tools.
