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.

Integrative variable selection via Bayesian model uncertainty

We are interested in developing integrative approaches for variable selection problems that incorporate external knowledge on a set of predictors of interest. In particular, we have developed an integrative Bayesian model uncertainty (iBMU) method, which formally incorporates multiple sources of data via a second-stage probit model on the probability that any predictor is associated with the outcome of interest. Using simulations, we demonstrate that iBMU leads to an increase in power to detect true marginal associations over more commonly used variable selection techniques, such as least absolute shrinkage and selection operator and elastic net. In addition, iBMU leads to a more efficient model search algorithm over the basic BMU method even when the predictor-level covariates are only modestly informative. The increase in power and efficiency of our method becomes more substantial as the predictor-level covariates become more informative. Finally, we demonstrate the power and flexibility of iBMU for integrating both gene structure and functional biomarker information into a candidate gene study investigating over 50 genes in the brain reward system and their role with smoking cessation from the Pharmacogenetics of Nicotine Addiction and Treatment Consortium.

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.

Dental mineralization

Extracellular matrix components and cell-derived microstructures are implicated in mineralization processes which occur in dental tissues. The respective role(s) of collagenic and non-collagenic matrix components are reviewed: phosphorylated and non-phosphorylated proteins, proteoglycans and phosphpholipids. Space-filling amphiphilic molecules seem to play an important role in the preorganization and oriented deposition of calcium phosphate on structures serving more or less as passive support in dentine as well as in enamel.

Effects of indomethacin and diclofenac on cerebral blood flow in hypercapnic conscious rats

Cerebral blood flow (CBF) was measured in conscious rats treated with indomethacin, diclofenac or the vehicle for indomethacin during hypercapnia of three different degrees. Similar values were found for CBF in rats treated with diclofenac or vehicle and there was a direct correlation between CBF and the arterial blood carbon dioxide values. In contrast, indomethacin completely blocked the increase of CBF during hypercapnia. These results suggest strongly that prostaglandins are not involved in the hypercapnia-induced cerebral vasodilatation.

Effects of aspirin and indomethacin on cerebral circulation in the conscious rat: evidence for a physiological role of endogenous prostaglandins

The purpose of this work was to evaluate the effects of equipotent doses of two different inhibitors of cyclo-oxygenase, indomethacin and aspirin, on cerebral blood flow and cerebral vascular resistances in the conscious undisturbed rat, using the reference sample radioactive microsphere method. We found that both, aspirin (50 mg/kg) and indomethacin (5 mg/kg) at 3, 15 and 60 min after their intravenous administration, increased cerebral vascular resistances and decreased cerebral blood flow to a similar extent. Both drugs completely abolished the hypotensive effect of 5 mg/kg i.v. arachidonic acid and they did not change arterial PO2, PCO2 or pH values. We conclude that the pharmacological inhibition of cyclooxygenase in the conscious undisturbed rat leads to a cerebral vasoconstriction and consequently to a decrease in cerebral blood flow. Our results evidence that prostaglandins are a physiological factor that actively contributes to the maintenance of cerebral circulation.