Optimal adaptive promising zone designs.

We develop optimal decision rules for sample size re-estimation in two-stage adaptive group sequential clinical trials. It is usual for the initial sample size specification of such trials to be adequate to detect a realistic treatment effect with good power, but not sufficient to detect the smallest clinically meaningful treatment effect . Moreover it is difficult for the sponsors of such trials to make the up-front commitment needed to adequately power a study to detect .

Semiparametric Bayesian latent variable regression for skewed multivariate data.

For many real-life studies with skewed multivariate responses, the level of skewness and association structure assumptions are essential for evaluating the covariate effects on the response and its predictive distribution. We present a novel semiparametric multivariate model and associated Bayesian analysis for multivariate skewed responses. Similar to multivariate Gaussian densities, this multivariate model is closed under marginalization, allows a wide class of multivariate associations, and has meaningful physical interpretations of skewness levels and covariate effects on the marginal density.