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 t...
This article considers the problem of assessing causal effect moderation in longitudinal settings in...
Adaptive treatment regime is a set of rules that governs the assignment of time-varying treatment ba...
International audienceBackground: The description of adherence based on medication refill histories ...
We propose nested latent compliance class models for analyzing longitudinal randomized trials when s...
This article discusses a nested latent class model for analyzing longitudinal randomized trials when...
We consider a Markov structure for partially unobserved time-varying compliance classes in the Imben...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108032/1/sim5811.pd
Much research in the social and health sciences aims to understand the causal relationship between a...
Background: Medication adherence is a major obstacle to improving health care outcomes in long-term ...
Non-compliance in long-term cohort studies contributes to bias in the estimation of study parameters...
In the presence of non-compliance, conventional analysis by intention-to-treat provides an unbiased ...
We present a random effects logistic approach for estimating the efficacy of treatment for compliers...
AbstractBackgroundThere is considerable interest in adjusting for suboptimal adherence in randomized...
Exposure-crossover design offers a non-experimental option to control for stable baseline confoundin...
Longitudinal data have been collected in many medical studies. For this kind of data, observations w...
This article considers the problem of assessing causal effect moderation in longitudinal settings in...
Adaptive treatment regime is a set of rules that governs the assignment of time-varying treatment ba...
International audienceBackground: The description of adherence based on medication refill histories ...
We propose nested latent compliance class models for analyzing longitudinal randomized trials when s...
This article discusses a nested latent class model for analyzing longitudinal randomized trials when...
We consider a Markov structure for partially unobserved time-varying compliance classes in the Imben...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108032/1/sim5811.pd
Much research in the social and health sciences aims to understand the causal relationship between a...
Background: Medication adherence is a major obstacle to improving health care outcomes in long-term ...
Non-compliance in long-term cohort studies contributes to bias in the estimation of study parameters...
In the presence of non-compliance, conventional analysis by intention-to-treat provides an unbiased ...
We present a random effects logistic approach for estimating the efficacy of treatment for compliers...
AbstractBackgroundThere is considerable interest in adjusting for suboptimal adherence in randomized...
Exposure-crossover design offers a non-experimental option to control for stable baseline confoundin...
Longitudinal data have been collected in many medical studies. For this kind of data, observations w...
This article considers the problem of assessing causal effect moderation in longitudinal settings in...
Adaptive treatment regime is a set of rules that governs the assignment of time-varying treatment ba...
International audienceBackground: The description of adherence based on medication refill histories ...