首页 | 本学科首页   官方微博 | 高级检索  
   检索      


Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout
Authors:Daniels M J  Hogan J W
Institution:Department of Statistics, Iowa State University, 102G Snedecor Hall, Ames, Iowa 50011, USA. mdaniels@iastate.edu
Abstract:Pattern mixture models are frequently used to analyze longitudinal data where missingness is induced by dropout. For measured responses, it is typical to model the complete data as a mixture of multivariate normal distributions, where mixing is done over the dropout distribution. Fully parameterized pattern mixture models are not identified by incomplete data; Little (1993, Journal of the American Statistical Association 88, 125-134) has characterized several identifying restrictions that can be used for model fitting. We propose a reparameterization of the pattern mixture model that allows investigation of sensitivity to assumptions about nonidentified parameters in both the mean and variance, allows consideration of a wide range of nonignorable missing-data mechanisms, and has intuitive appeal for eliciting plausible missing-data mechanisms. The parameterization makes clear an advantage of pattern mixture models over parametric selection models, namely that the missing-data mechanism can be varied without affecting the marginal distribution of the observed data. To illustrate the utility of the new parameterization, we analyze data from a recent clinical trial of growth hormone for maintaining muscle strength in the elderly. Dropout occurs at a high rate and is potentially informative. We undertake a detailed sensitivity analysis to understand the impact of missing-data assumptions on the inference about the effects of growth hormone on muscle strength.
Keywords:Aging research  Clinical trial  Identifiability  Intention to treat  Longitudinal data  Missing data  Muscle strength  Nonignorable nonresponse  Recombinant human growth hormone  Repeated measures  Selection bias
本文献已被 PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号