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

TitleBayesian modeling and inference for clinical trials with partial retrieved data following dropout.
Publication TypeJournal Article
Year of Publication2013
AuthorsChen, Qingxia, Ming-Hui Chen, David Ohlssen, and Joseph G. Ibrahim
JournalStat Med
Volume32
Issue24
Pagination4180-95
Date Published2013 Oct 30
ISSN1097-0258
KeywordsAlgorithms, Bayes Theorem, Cognitive Dysfunction, Data Interpretation, Statistical, Humans, Markov Chains, Models, Statistical, Monte Carlo Method, Patient Dropouts, Phenylcarbamates, Randomized Controlled Trials as Topic, Rivastigmine
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 model for repeated measurements from the assigned treatments and the standard treatment, a multivariate logistic regression model for those stopping the assigned treatments, logistic regression models for those starting a standard treatment off protocol, and a conditional multivariate logistic regression model for completely withdrawing from the study. We assume that withdrawing from the study is non-ignorable, but intermittent missingness is assumed to be at random. We examine various properties of the proposed model. We develop an efficient Markov chain Monte Carlo sampling algorithm. We analyze in detail via the proposed method a real dataset from a clinical trial.

DOI10.1002/sim.5812
Alternate JournalStat Med
Original PublicationBayesian modeling and inference for clinical trials with partial retrieved data following dropout.
PubMed ID23620446
PubMed Central IDPMC3796028
Grant ListR21 HL097334 / HL / NHLBI NIH HHS / United States
CA 74015 / CA / NCI NIH HHS / United States
1R21HL097334 / HL / NHLBI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
GM 70335 / GM / NIGMS NIH HHS / United States
UL1 RR024975 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
UL1 RR024975-01 / RR / NCRR NIH HHS / United States
R01 CA074015 / CA / NCI NIH HHS / United States
Project: