Title | Bayesian modeling and inference for clinical trials with partial retrieved data following dropout. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Chen, Qingxia, Ming-Hui Chen, David Ohlssen, and Joseph G. Ibrahim |
Journal | Stat Med |
Volume | 32 |
Issue | 24 |
Pagination | 4180-95 |
Date Published | 2013 Oct 30 |
ISSN | 1097-0258 |
Keywords | Algorithms, 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. |
DOI | 10.1002/sim.5812 |
Alternate Journal | Stat Med |
Original Publication | Bayesian modeling and inference for clinical trials with partial retrieved data following dropout. |
PubMed ID | 23620446 |
PubMed Central ID | PMC3796028 |
Grant List | R21 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 |
Bayesian modeling and inference for clinical trials with partial retrieved data following dropout.
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