Continuous endpoints in Bayesian two-stage designs.

Single-arm two-stage designs for phase II of clinical trials typically focus on a binary endpoint obtained by dichotomizing an underlying continuous measure of treatment efficacy. To avoid the resulting loss of information, we propose a two-stage design based on a Bayesian predictive approach that directly uses the original continuous endpoint. Numerical results are provided with reference to phase II cancer trials aimed at assessing tumor shrinking effect of an experimental treatment.

Reference Charts for Fetal Cerebellar Vermis Height: A Prospective Cross-Sectional Study of 10605 Fetuses.

Objective: To establish reference charts for fetal cerebellar vermis height in an unselected population.

Methods: A prospective cross-sectional study between September 2009 and December 2014 was carried out at ALTAMEDICA Fetal-Maternal Medical Centre, Rome, Italy. Of 25203 fetal biometric measurements, 12167 (48%) measurements of the cerebellar vermis were available.

Reference charts for fetal corpus callosum length: a prospective cross-sectional study of 2950 fetuses.

Objectives: The purpose of this study was to establish reference charts for fetal corpus callosum length in a convenience sample.

Methods: A prospective cross-sectional study was conducted at the Artemisia Fetal-Maternal Medical Center between December 2008 and January 2012. Among 16,975 fetal biometric measurements between 19 weeks and 37 weeks 6 days' gestation, 3438 measurements of the corpus callosum (20.

Mixtures of prior distributions for predictive Bayesian sample size calculations in clinical trials.

In this paper we propose a predictive Bayesian approach to sample size determination (SSD) and re-estimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically a treatment effect or an effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors.

Robust Bayesian sample size determination in clinical trials.

This article deals with determination of a sample size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward sample size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large.