Bayesian enrichment strategies for randomized discontinuation trials.
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ABSTRACT:
SUBMITTER: Trippa L
PROVIDER: S-EPMC3667626 | biostudies-literature | 2012 Mar
REPOSITORIES: biostudies-literature
Publications
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]
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Project description:BACKGROUND: The Cochrane Highly Sensitive Search Strategy (HSSS), which contains three phases, is widely used to identify Randomized Controlled Trials (RCTs) in MEDLINE. Lefebvre and Clarke suggest that reviewers might consider using four revisions of the HSSS. The objective of this study is to validate these four revisions: combining the free text terms volunteer, crossover, versus, and the Medical Subject Heading CROSS-OVER STUDIES with the top two phases of the HSSS, respectively. METHODS: We replicated the subject search for 61 Cochrane reviews. The included studies of each review that were indexed in MEDLINE were pooled together by review and then combined with the subject search and each of the four proposed search strategies, the top two phases of the HSSS, and all three phases of the HSSS. These retrievals were used to calculate the sensitivity and precision of each of the six search strategies for each review. RESULTS: Across the 61 reviews, the search term versus combined with the top two phases of the HSSS was able to find 3 more included studies than the top two phases of the HSSS alone, or in combination with any of the other proposed search terms, but at the expense of missing 56 relevant articles that would be found if all three phases of the HSSS were used. The estimated time needed to finish a review is 1086 hours for all three phases of the HSSS, 823 hours for the strategy versus, 818 hours for the first two phases of the HSSS or any of the other three proposed strategies. CONCLUSION: This study shows that compared to the first two phases of the HSSS, adding the term versus to the top two phases of the HSSS balances the sensitivity and precision in the reviews studied here to some extent but the differences are very small. It is well known that missing relevant studies may result in bias in systematic reviews. Reviewers need to weigh the trade-offs when selecting the search strategies for identifying RCTs in MEDLINE.
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Project description:Researchers often have informative hypotheses in mind when comparing means across treatment groups, such as H1 : μA < μB < μC and H2 : μB < μA < μC, and want to compare these hypotheses to each other directly. This can be done by means of Bayesian inference. This article discusses the disadvantages of the frequentist approach to null hypothesis testing and the advantages of the Bayesian approach. It demonstrates how to use the Bayesian approach to hypothesis testing in the setting of cluster-randomized trials. The data from a school-based smoking prevention intervention with four treatment groups are used to illustrate the Bayesian approach. The main advantage of the Bayesian approach is that it provides a degree of evidence from the collected data in favor of an informative hypothesis. Furthermore, a simulation study was conducted to investigate how Bayes factors behave with cluster-randomized trials. The results from the simulation study showed that the Bayes factor increases with increasing number of clusters, cluster size, and effect size, and decreases with increasing intraclass correlation coefficient. The effect of the number of clusters is stronger than the effect of cluster size. With a small number of clusters, the effect of increasing cluster size may be weak, especially when the intraclass correlation coefficient is large. In conclusion, the study showed that the Bayes factor is affected by sample size and intraclass correlation similarly to how these parameters affect statistical power in the frequentist approach of null hypothesis significance testing. Bayesian evaluation may be used as an alternative to null hypotheses testing.
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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.
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Project description:Genomics and Randomized Trials Network (GARNET)
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Project description:BackgroundHeterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.MethodsThis article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.ResultsSimulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.ConclusionWe proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.
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