Utility-Based Dose Selection for Phase II Dose-Finding Studies.

BACKGROUND AND OBJECTIVES: Dose selection is a key feature of clinical development. Poor dose selection has been recognized as a major driver of development failure in late phase. It usually involves both efficacy and safety criteria. The objective of this paper is to develop and implement a novel fully Bayesian statistical framework to optimize the dose selection process by maximizing the expected utility in phase III. METHODS: The success probability is characterized by means of a utility function with two components, one for efficacy and one for safety. Each component refers to a dose-response model. Moreover, a sequential design (with futility and efficacy rules at the interim analysis) is compared to a fixed design in order to allow one to hasten the decision to perform the late phase study. Operating characteristics of this approach are extensively assessed by simulations under a wide range of dose-response scenarios. RESULTS AND CONCLUSIONS: Simulation results illustrate the difficulty of simultaneously estimating two complex dose-response models with enough accuracy to properly rank doses using an utility function combining the two. The probability of making the good decision increases with the sample size. For some scenarios, the sequential design has good properties: with a quite large probability of study termination at interim analysis, it enables to reduce the sample size while maintaining the properties of the fixed design.

Design optimization for dose-finding trials: a review.

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields.

As most georeferenced data sets are multivariate and concern variables of different types, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the modeling of the dependence between processes. The aim of this article is to present a new hierarchical Bayesian approach that permits simultaneous modeling of dependent Gaussian, count, and ordinal spatial fields. This approach is based on spatial generalized linear mixed models. We use a moving average approach to model the spatial dependence between the processes. The method is first validated through a simulation study. We show that the multivariate model has better predictive abilities than the univariate one. Then the multivariate spatial hierarchical model is applied to a real data set collected in French Guiana to predict topsoil patterns.

Alcohol and atherosclerotic vascular disease risk factors in French men: relationships are linear, J-shaped, and U-shaped.

BACKGROUND: Although it is well admitted that alcohol displays a U-shaped relationship with atherosclerotic vascular disease, individual relationships between alcohol and atherosclerosis risk factors may be different and have not been determined precisely for several of them. METHODS: A cross-sectional study within the SU.VI.MAX French cohort study was performed to assess the curve of potential relationships between alcohol and atherosclerosis risk factors in 2126 healthy men. Mean daily alcohol intake was derived from 37 alcoholic beverages in twelve 24-hr dietary recalls. Logistic models were adjusted for age. RESULTS: Apolipoprotein B (ApoB), fasting glucose, body mass index, waist-to-hip ratio, and waist circumference displayed a linear relationship with alcohol. The odds ratios and 95% confidence intervals associated with abnormal values of the markers for the highest quintile of alcohol intake were 1.45 (1.06-1.97) for ApoB, 1.98 (1.40-2.80) for fasting glucose, and 1.74 (1.30-2.34) for body mass index. An inverse J-shaped relationship was assumed for ApoA1 and ApoB/ApoA1 ratio, whereas a U-shaped relationship was observed for serum triglycerides and mixed hyperlipidemia. Only the highest quintile of alcohol was associated with hypertension, although the test for linearity was also significant. No association was observed for Lp(a) or homocysteine. Associations were unmodified by further adjustment for carbohydrates, fiber, lipids, tobacco, or exercise. CONCLUSIONS: The aggregate of the disparate alcohol risk factor relationships suggests probable net benefit at 15 to 25 g of alcohol/day.

Drinking patterns in French adult men--a cluster analysis of alcoholic beverages and relationship with lifestyle.

BACKGROUND: Establishing patterns of alcohol consumption may be useful for investigating the relationship between alcohol and diseases. METHODS: We used a hierarchical agglomerative clustering method to describe the intake of eight types of alcoholic beverages and to determine drinking patterns in a cohort of 1797 men enrolled in a French 8-year intervention study involving nutritional doses of vitamins and minerals, the SU.VI.MAX study. RESULTS: Cluster 1, referred to as 'abstainers', was defined a priori and included 329 men who drank less than 5 g of alcohol per day. Six drinking patterns were defined in alcohol drinkers, with increasing mean alcohol intake: cluster 2, 'low drinkers', included 670 subjects, who drank little of any type of alcoholic beverage; cluster 3, 'high quality wines', included 584 men with a high intake of champagne, high quality wines, and high-alcohol aperitifs; cluster 4, 'beer and cider', included 190 subjects with a high intake of beer and cider; cluster 5, 'digestives', included 54 men with a specifically high consumption of digestive beverages; cluster 6, 'local wines', included 238 subjects with a high intake of local wines and low-alcohol aperitifs; cluster 7, 'table wines', included 61 men with a high intake of table wines and high-alcohol aperitifs. These clusters were significantly associated with socioeconomic and lifestyle variables such as place of residence, occupation, mean caloric intake and distribution of energy intake throughout the day, body mass index, and smoking habits. CONCLUSIONS: They will be useful in future studies of the relationship between alcohol intake and medical conditions or risk factors.