Citation

Lin JY, ten Have TR, Bogner HR, Elliott MR. Stat. Med. 2007; 26(28): 5100-5115.

Copyright

(Copyright © 2007, John Wiley and Sons)

DOI

10.1002/sim.2909

PMID

17477334

PMCID

PMC2810145

Abstract

Lin et al. (http://www.biostatsresearch.com/upennbiostat/papers/, 2006) proposed a nested Markov compliance class model in the Imbens and Rubin compliance class model framework to account for time-varying subject noncompliance in longitudinal randomized intervention studies. We use superclasses, or latent compliance class principal strata, to describe longitudinal compliance patterns, and time-varying compliance classes are assumed to depend on the history of compliance. In this paper, we search for good subject-level baseline predictors of these superclasses and also examine the relationship between these superclasses and all-cause mortality. Since the superclasses are completely latent in all subjects, we utilize multiple imputation techniques to draw inferences. We apply this approach to a randomized intervention study for elderly primary care patients with depression.

Language: en

Keywords

Aged; Depressive Disorder; Humans; Longitudinal Studies; Markov Chains; Models, Statistical; Mortality; Patient Dropouts; Patient Selection; Proportional Hazards Models; Randomized Controlled Trials as Topic; Suicide Prevention; Time Factors