title: Simulation-based sample-sizing and power calculations in logistic regression with partial prior information.
abstract: There have been many approximations developed for sample sizing of a logistic regression model with a single normally-distributed stimulus. Despite this, it has been recognised that there is no consensus as to the best method. In pharmaceutical drug development, simulation provides a powerful tool to characterise the operating characteristics of complex adaptive designs and is an ideal method for determining the sample size for such a problem. In this paper, we address some issues associated with applying simulation to determine the sample size for a given power in the context of logistic regression. These include efficient methods for evaluating the convolution of a logistic function and a normal density and an efficient heuristic approach to searching for the appropriate sample size. We illustrate our approach with three case studies. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Adaptive Design Innovation Centre, Icon PLC, Marlow, UK.
authors: Andrew P
authors: 0000-0003-3143-2554
authors: AP
authors: Grieve
authors: Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, London, UK.
authors: Shah-Jalal
authors: SJ
authors: Sarker
mesh_terms: D003198:Computer Simulation; D015195:Drug Design; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size
keywords: convolution; logistic regression; orthogonal polynomials; sample sizing; simulation