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Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments.

ABSTRACT: The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adjustment.We apply an estimator for the average treatment effect in the trial that adjusts for prognostic baseline variables to improve precision [1]. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct simulation studies based on resampling genomic and clinical data of breast cancer patients from four publicly available observational studies.Separate simulation studies were conducted based on each of the four data sets, with sample sizes ranging from 115 to 307. Adjusting only for clinical variables provided gains of -1%, 5%, 6%, 17%, compared to the unadjusted estimator. Adjusting only for the MammaPrint genomic signature provided gains of 2%, 4%, 4%, 5%. Simultaneously adjusting for clinical variables and the genomic signature provided gains of 2%, 6%, 7%, 16%. The differences between precision gains from adjusting for genomic plus clinical variables, versus only clinical variables, were -1%, 0%, 1%, 3%.Adjusting only for clinical variables led to substantial precision gains (at least 5%) in three of the four data sets, with a 1% precision loss in the remaining data set. These gains were unchanged or increased when sample sizes were doubled in our simulations. The precision gains due to incorporating genomic information, beyond the gains from adjusting for clinical variables, were not substantial.

SUBMITTER: Patil P

PROVIDER: S-EPMC5935844 | biostudies-other | 2016 Aug

REPOSITORIES: biostudies-other

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Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments.

Patil Prasad P Colantuoni Elizabeth E Leek Jeffrey T JT Rosenblum Michael M

Contemporary clinical trials communications 20160331

<h4>Background</h4>The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adj ...[more]

PMID: 29736456

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Project description:BackgroundEndpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" versus "not recovered."MethodsWe evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials.ResultsPower for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time.DiscussionTime-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.

Project description:Clinical trials testing infertility treatments often do not report on the major outcomes of interest to patients and clinicians and the public (such as live birth) nor on the harms, including maternal risks during pregnancy and fetal anomalies. This is complicated by the multiple participants in infertility trials which may include a woman (mother), a man (father), and result in a third individual if successful, their offspring (child), who is also the desired outcome of treatment. The primary outcome of interest and many adverse events occur after cessation of infertility treatment and during pregnancy and the puerperium, which create a unique burden of follow-up for clinical trial investigators and participants. In 2013, because of the inconsistencies in trial reporting and the unique aspects of infertility trials not adequately addressed by existing Consolidated Standards of Reporting Trials (CONSORT) statements, we convened a consensus conference in Harbin, China, with the aim of planning modifications to the CONSORT checklist to improve the quality of reporting of clinical trials testing infertility treatment. The consensus group recommended that the preferred primary outcome of all infertility trials is live birth (defined as any delivery of a live infant ?20 weeks gestations) or cumulative live birth, defined as the live birth per women over a defined time period (or number of treatment cycles). In addition, harms to all participants should be systematically collected and reported, including during the intervention, any resulting pregnancy, and during the neonatal period. Routine information should be collected and reported on both male and female participants in the trial. We propose to track the change in quality that these guidelines may produce in published trials testing infertility treatments. Our ultimate goal is to increase the transparency of benefits and risks of infertility treatments to provide better medical care to affected individuals and couples.

Dataset Information

Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments.

Publications

Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments.

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