Clinical trials : journal of the Society for Clinical Trials
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In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. ⋯ Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.
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Attrition is a serious problem in many clinical trials. The practice of offering completion bonuses-financial incentives offered to participants on the condition that they remain in a trial until they reach a prespecified study endpoint-is one means of addressing attrition. ⋯ Nonetheless, because completion bonuses may in some cases still encourage unreasonable continued participation in a study, additional safeguards are appropriate. Rejecting completion bonuses entirely is, however, unnecessary and would problematically fail to address the significant ethical problems associated with participant attrition.