Mon, Jan 23, 2017 at noon:
H. Luke Shaefer
Lin, J.Y., T.R. Ten Have, and Michael R. Elliott. 2008. "Longitudinal nested compliance class model in the presence of time-varying noncompliance." Journal of the American Statistical Association, 103(482): 462-473.
This article discusses a nested latent class model for analyzing longitudinal randomized trials when subjects do not always adhere to the treatment to which they are randomized. In the Prevention of Suicide in Primary Care Elderly: Collaborative Trial, subjects were randomized to either the control treatment, where they received standard care, or to the intervention, where they received standard care in addition to meeting with depression health specialists. The health specialists educate patients, their families, and physicians about depression and monitor their treatment. Those randomized to the control treatment have no access to the health specialists; however, those randomized to the intervention could choose not to meet with the health specialists, hence receiving only the standard care. Subjects participated in the study for two years where depression severity and adherence to meeting with health specialists were measured at each follow-up. The outcome of interest is the effect of meeting with the health specialists on depression severity. Traditional intention-to-treat and as-treated analyses may produce biased causal effect estimates in the presence of subject noncompliance. Utilizing a nested latent class model that uses subject-specific and time-invariant "superclasses" allows us to summarize longitudinal trends of compliance patterns and to estimate the effect of the intervention using intention-to-treat contrasts within principal strata that correspond to longitudinal compliance behavior patterns. Analyses show that subjects with more severe depression are more likely to adhere to treatment randomization, and those that are compliant and meet with health specialists benefit from the meetings and show improvement in depression. Simulation results show that our estimation procedure produces reasonable parameter estimates under correct model assumptions.
PMCID: PMC2700859. (Pub Med Central)