Bias in 2-part mixed models for longitudinal semicontinuous data.

ABSTRACT: Semicontinuous data in the form of a mixture of zeros and continuously distributed positive values frequently arise in biomedical research. Two-part mixed models with correlated random effects are an attractive approach to characterize the complex structure of longitudinal semicontinuous data. In practice, however, an independence assumption about random effects in these models may often be made for convenience and computational feasibility. In this article, we show that bias can be induced for regression coefficients when random effects are truly correlated but misspecified as independent in a 2-part mixed model. Paralleling work on bias under nonignorable missingness within a shared parameter model, we derive and investigate the asymptotic bias in selected settings for misspecified 2-par

SUBMITTER: Su L 

PROVIDER: S-EPMC2648907 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature