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Bayesian enrichment strategies for randomized discontinuation trials.

ABSTRACT: We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

SUBMITTER: Trippa L

PROVIDER: S-EPMC3667626 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Bayesian enrichment strategies for randomized discontinuation trials.

Trippa Lorenzo L Rosner Gary L GL Müller Peter P

Biometrics 20110629 1

We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the ...[more]

PMID: 21714780

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Project description:In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.

Project description:ImportanceRandomized clinical trials (RCTs) play an important role in clinical decision-making, and discontinuation or nonpublication of these trials are causes of great concern. The extent of discontinued or unpublished RCTs about head and neck cancer remains unclear.ObjectiveTo assess the rate of discontinuation or nonpublication of RCTs involving patients with head and neck cancer. This objective was measured by observing 3 domains: discontinuation of trial, nonpublication of trial data, and feasibility of contacting trial investigators of aforementioned trials.Evidence reviewFor this study, the sample was derived using the ClinicalTrials.gov advanced search feature on March 18, 2019, to locate completed and discontinued RCTs pertaining to head and neck cancer registered before this date. Trials were analyzed to identify reasons for trial discontinuation and publication status of each trial. If publication status or reason for trial discontinuation was not allocated through the systematic search of ClinicalTrials.gov, the corresponding author was emailed to determine publication status.FindingsAfter exclusions, 130 RCTs were included. Of these trials, 92 (70.8%) were completed and 38 (29.2%) were discontinued for various reasons. The most common reason for discontinuation of trials was committee recommendations. Of the 130 analyzed trials, 67 (51.5%) were published in a peer-reviewed journal and 63 (48.5%) were unpublished trials. Of the 92 completed trials, 55 (59.8%) were published and 37 (40.2%) remained unpublished 3 or more years after trial completion. Trials funded by other sources (private, nonprofit, or the National Institutes of Health) were more likely to reach publication than industry-funded RCTs (unadjusted odds ratio, 4.3 [95% CI, 1.3-14.0]; adjusted odds ratio, 4.1 [95% CI, 1.2-14.3]).Conclusions and relevanceOf RCTs in head and neck cancer, 29.2% were discontinued and 40.2% completed trials never reached publication. The findings suggest that needs exist for RCT guidance of head and neck cancer. The reporting of reasons for trial discontinuation appears to be lacking, and trial publication rates were low. This study is relevant to many physicians and researchers because it identifies potential sources of decreased research productivity and ethics.

Project description:Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha?=?2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.

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Bayesian enrichment strategies for randomized discontinuation trials.

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Bayesian enrichment strategies for randomized discontinuation trials.

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