Bayesian adaptive design for clinical trials in Duchenne muscular dystrophy.

A Bayesian adaptive design is proposed for a clinical trial in Duchenne muscular dystrophy. The trial was designed to demonstrate treatment efficacy on an ambulatory-based clinical endpoint and to identify early success on a biomarker (dystrophin protein levels) that can serve as a basis for accelerated approval in the United States. The trial incorporates placebo augmentation using placebo data from past clinical trials. A thorough simulation study was conducted to understand the operating characteristics of the trial. This trial design was selected for the US FDA Complex Innovative Trial Design Pilot Meeting Program and the experience in that program is summarized.

Incorporating prior biologic information for high-dimensional rare variant association studies.

BACKGROUND: Given the increasing scale of rare variant association studies, we introduce a method for high-dimensional studies that integrates multiple sources of data as well as allows for multiple region-specific risk indices. METHODS: Our method builds upon the previous Bayesian risk index by integrating external biological variant-specific covariates to help guide the selection of associated variants and regions. Our extension also incorporates a second level of uncertainty as to which regions are associated with the outcome of interest. RESULTS: Using a set of study-based simulations, we show that our approach leads to an increase in power to detect true associations in comparison to several commonly used alternatives. Additionally, the method provides multi-level inference at the pathway, region and variant levels. CONCLUSION: To demonstrate the flexibility of the method to incorporate various types of information and the applicability to high-dimensional data, we apply our method to a single region within a candidate gene study of second primary breast cancer and to multiple regions within a candidate pathway study of colon cancer.

Incorporating model uncertainty in detecting rare variants: the Bayesian risk index.

We are interested in investigating the involvement of multiple rare variants within a given region by conducting analyses of individual regions with two goals: (1) to determine if regional rare variation in aggregate is associated with risk; and (2) conditional upon the region being associated, to identify specific genetic variants within the region that are driving the association. In particular, we seek a formal integrated analysis that achieves both of our goals. For rare variants with low minor allele frequencies, there is very little power to statistically test the null hypothesis of equal allele or genotype counts for each variant. Thus, genetic association studies are often limited to detecting association within a subset of the common genetic markers. However, it is very likely that associations exist for the rare variants that may not be captured by the set of common markers. Our framework aims at constructing a risk index based on multiple rare variants within a region. Our analytical strategy is novel in that we use a Bayesian approach to incorporate model uncertainty in the selection of variants to include in the index as well as the direction of the associated effects. Additionally, the approach allows for inference at both the group and variant-specific levels. Using a set of simulations, we show that our methodology has added power over other popular rare variant methods to detect global associations. In addition, we apply the approach to sequence data from the WECARE Study of second primary breast cancers.