Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 30, Issue 3, Pages 799-815Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280220975790
Keywords
Clinical trials; simulation; sample size; power; Gaussian process; global optimisation
Categories
Funding
- Medical Research Council [MR/N015444/1]
- MRC [MR/N015444/1] Funding Source: UKRI
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The method proposes a general framework for solving simulation-based sample size determination problems with multiple design parameters and conflicting criteria to be minimized. It utilizes a global optimization algorithm and a non-parametric regression model to approximate the true underlying power function, making it flexible and applicable to a wide range of simulation-based power estimation problems.
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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