Efficient and flexible simulation-based sample size determination for clinical trials with multiple design parameters
Year: 2020
Type: article
Abstract: Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of sample size determination problems, often minimising a single parameter (the overall sample size) subject to power being above a target level. We describe a general framework for solving simulation-based sample size determination problems with several design parameters over which to optimise and several conflicting criteria to be minimised. The method is based on an established global optimisation algorithm widely used in the design and analysis of computer experiments, using a non-parametric regression model as an approximation of the true underlying power function. The method is flexible, can be used for almost any problem for which power can be estimated using simulation, and can be implemented using existing statistical software packages. We illustrate its application to a sample size determination problem involving complex clustering structures, two primary endpoints and small sample considerations.
Authors: Duncan T Wilson, Richard Hooper, Julia Brown, Amanda J Farrin, Rebecca EA Walwyn
Institutions: University of Leeds, Queen Mary University of London
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
Funder: Medical Research Council
OpenAlex ID: W2969525494