Simulation‐based sample‐sizing and power calculations in logistic regression with partial prior information. (2nd September 2016)

• Record Type: Journal Article
• Authors: Grieve, Andrew P.; Sarker, Shah‐Jalal
• Is Part Of: Pharmaceutical statistics. Volume 15:Number 6(2016)
• Page Start: 507
• Page End: 516
• Publication Date: 2016-09-02
• Subjects: logistic regression -- sample sizing -- convolution -- simulation -- orthogonal polynomials; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1773
• Languages: English
• ISSNs: 1539-1604

Abstract

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.