Efficient and flexible simulation-based sample size determination for clinical trials with multiple design parameters

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...

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Bibliographic Details
Published in:Statistical methods in medical research Vol. 30; no. 3; p. 799
Main Authors: Wilson, Duncan T, Hooper, Richard, Brown, Julia, Farrin, Amanda J, Walwyn, Rebecca Ea
Format: Journal Article
Language:English
Published: England 01.03.2021
Subjects:
Algorithms
Cluster Analysis
Computer Simulation
Models, Statistical
Research Design
Sample Size
Clinical trials
Gaussian process
global optimisation
power
sample size
simulation
ISSN:1477-0334, 1477-0334
Online Access:Get more information
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Summary: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.
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ISSN:1477-0334
1477-0334
DOI:10.1177/0962280220975790