Abstract
| DATE: | December 1, 2011 |
| TIME: | 1:15 - 3:00 pm |
| LOCATION: | Center for Clinical Sciences Research (CCSR), Rm 4205 |
| TITLE: | Longitudinal Causal Model in the Principal Stratification Framework |
| SPEAKER: | Julia Lin, Mathematicial Statistician Cooperative Studies Program, Coordinating Center, VA |
In this workshop we discuss nested latent compliance class models in the Imbens and Rubin (1997) principal
stratification framework for analyzing longitudinal randomized trials when subjects do not always adhere to the
treatment to which they are randomized, and treatment adherence may vary over time. Traditional “intention-to-treat” and “as-treated”
analyses may produce biased causal effect estimates in the presence of subject noncompliance. Utilizing nested latent compliance
class models that use subject-specific and time-invariant “superclasses” allow us to summarize longitudinal trends of compliance
patterns, and estimate the effect of the intervention using “intent-to-treat” contrasts within principal strata (PS) corresponding
to longitudinal compliance behavior patterns. First we discuss a conditional independence model where the time-varying compliance
classes within a subject are assumed to be independent given compliance superclass and covariates. Then we propose a Markov model
where time-varying compliance classes are assumed to relate to history of compliance, compliance superclass, and covariates.
We illustrate these models with analyses of the Prevention of Suicide in Primary Care Elderly: Collaborative Trial (PROSPECT),
a randomized intervention study of elderly patients in primary care clinics with depression.
*Co-sponsored with the
San Francisco Bay Area Chapter of the American Statistical Association
Suggested reading:
Imbens, G.W., Rubin, D.B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance.
The Annals of Statistics,
25(1), 305-327.
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