Home  About us  Contact
 

Expected Sample Size (expected + sample_size)

Distribution by Scientific Domains
Mathematics and Statistics100%

Selected Abstracts

Optimal Spending Functions for Asymmetric Group Sequential Designs

BIOMETRICAL JOURNAL, Issue 3 2007
Keaven M. Anderson
Abstract We present optimized group sequential designs where testing of a single parameter , is of interest. We require specification of a loss function and of a prior distribution for ,. For the examples presented, we pre-specify Type I and II error rates and minimize the expected sample size over the prior distribution for ,. Minimizing the square of sample size rather than the sample size is found to produce designs with slightly less aggressive interim stopping rules and smaller maximum sample sizes with essentially identical expected sample size. We compare optimal designs using Hwang-Shih-DeCani and Kim-DeMets spending functions to fully optimized designs not restricted by a spending function family. In the examples selected, we also examine when there might be substantial benefit gained by adding an interim analysis. Finally, we provide specific optimal asymmetric spending function designs that should be generally useful and simply applied when a design with minimal expected sample size is desired. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

An Adaptive Two-stage Design with Treatment Selection Using the Conditional Error Function Approach

BIOMETRICAL JOURNAL, Issue 4 2006
Jixian Wang
Abstract As an approach to combining the phase II dose finding trial and phase III pivotal trials, we propose a two-stage adaptive design that selects the best among several treatments in the first stage and tests significance of the selected treatment in the second stage. The approach controls the type I error defined as the probability of selecting a treatment and claiming its significance when the selected treatment is indifferent from placebo, as considered in Bischoff and Miller (2005). Our approach uses the conditional error function and allows determining the conditional type I error function for the second stage based on information observed at the first stage in a similar way to that for an ordinary adaptive design without treatment selection. We examine properties such as expected sample size and stage-2 power of this design with a given type I error and a maximum stage-2 sample size under different hypothesis configurations. We also propose a method to find the optimal conditional error function of a simple parametric form to improve the performance of the design and have derived optimal designs under some hypothesis configurations. Application of this approach is illustrated by a hypothetical example. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

A COMPARISON OF THE IMPRECISE BETA CLASS, THE RANDOMIZED PLAY-THE-WINNER RULE AND THE TRIANGULAR TEST FOR CLINICAL TRIALS WITH BINARY RESPONSES

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2007
Lyle C. Gurrin
Summary This paper develops clinical trial designs that compare two treatments with a binary outcome. The imprecise beta class (IBC), a class of beta probability distributions, is used in a robust Bayesian framework to calculate posterior upper and lower expectations for treatment success rates using accumulating data. The posterior expectation for the difference in success rates can be used to decide when there is sufficient evidence for randomized treatment allocation to cease. This design is formally related to the randomized play-the-winner (RPW) design, an adaptive allocation scheme where randomization probabilities are updated sequentially to favour the treatment with the higher observed success rate. A connection is also made between the IBC and the sequential clinical trial design based on the triangular test. Theoretical and simulation results are presented to show that the expected sample sizes on the truly inferior arm are lower using the IBC compared with either the triangular test or the RPW design, and that the IBC performs well against established criteria involving error rates and the expected number of treatment failures. [source]