Bayesian modeling and inference for clinical trials with partial retrieved data following dropout.

ABSTRACT: In randomized clinical trials, it is common that patients may stop taking their assigned treatments and then switch to a standard treatment (standard of care available to the patient) but not the treatments under investigation. Although the availability of limited retrieved data on patients who switch to standard treatment, called off-protocol data, could be highly valuable in assessing the associated treatment effect with the experimental therapy, it leads to a complex data structure requiring the development of models that link the information of per-protocol data with the off-protocol data. In this paper, we develop a novel Bayesian method to jointly model longitudinal treatment measurements under various dropout scenarios. Specifically, we propose a multivariate normal mixed-effects mo

SUBMITTER: Chen Q 

PROVIDER: S-EPMC3796028 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature