Simulation‐based sample‐sizing and power calculations in logistic regression with partial prior information |
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Authors: | Andrew P. Grieve Shah‐Jalal Sarker |
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Affiliation: | 1. Adaptive Design Innovation Centre, Icon PLC, Marlow, UK;2. Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University 3. of London, London, UK |
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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. |
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Keywords: | logistic regression sample sizing convolution simulation orthogonal polynomials |
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