Subgroup identification in dose-finding trials via model-based recursive partitioning

An important task in early phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article we propose new strategies to perform subgroup analyses in dose-finding trials and discuss the challenges, which arise in this new setting. We consider model-based recursive partitioning, which has recently been applied to subgroup identification in two arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model-based recursive partitioning can be used to identify subgroups of patients with different dose-response curves and improves estimation of treatment effects and minimum effective doses, when heterogeneity among patients is present.Comment: 23 pages, 6 figure

Model Selection versus Model Averaging in Dose Finding Studies

Phase II dose finding studies in clinical drug development are typically conducted to adequately characterize the dose response relationship of a new drug. An important decision is then on the choice of a suitable dose response function to support dose selection for the subsequent Phase III studies. In this paper we compare different approaches for model selection and model averaging using mathematical properties as well as simulations. Accordingly, we review and illustrate asymptotic properties of model selection criteria and investigate their behavior when changing the sample size but keeping the effect size constant. In a large scale simulation study we investigate how the various approaches perform in realistically chosen settings. Finally, the different methods are illustrated with a recently conducted Phase II dosefinding study in patients with chronic obstructive pulmonary disease.Comment: Keywords and Phrases: Model selection; model averaging; clinical trials; simulation stud

Approximating Probability Densities by Iterated Laplace Approximations

The Laplace approximation is an old, but frequently used method to approximate integrals for Bayesian calculations. In this paper we develop an extension of the Laplace approximation, by applying it iteratively to the residual, i.e., the difference between the current approximation and the true function. The final approximation is thus a linear combination of multivariate normal densities, where the coefficients are chosen to achieve a good fit to the target distribution. We illustrate on real and artificial examples that the proposed procedure is a computationally efficient alternative to current approaches for approximation of multivariate probability densities. The R-package iterLap implementing the methods described in this article is available from the CRAN servers.Comment: to appear in Journal of Computational and Graphical Statistics, http://pubs.amstat.org/loi/jcg

MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies

In this article the MCPMod package for the R programming environment will be introduced. It implements a recently developed methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz et al. 2005). The MCPMod package provides tools for the analysis of dose finding trials, as well as a variety of tools necessary to plan an experiment to be analyzed using the MCP-Mod methodology

On the efficiency of adaptive designs

In this paper we develop a method to investigate the efficiency of two-stage adaptive designs from a theoretical point of view. Our approach is based on an explicit expansion of the information matrix for an adaptive design. The results enables one to compare the performance of adaptive designs with non-adaptive designs, without having to rely on extensive simulation studies. We demonstrate that their relative efficiency depends sensitively on the statistical problem under investigation and derive some general conclusions when to prefer an adaptive or a non-adaptive design. In particular, we show that in nonlinear regression models with moderate or large variances the first stage sample size of an adaptive design should be chosen sufficiently large in order to address variability in the interim parameter estimates. We illustrate the methodology with several examples