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

TL;DR

This paper introduces a flexible, simulation-based framework for optimizing multiple design parameters in clinical trial sample size determination, overcoming computational challenges of traditional methods.

Contribution

It presents a general optimization approach using global algorithms and non-parametric regression to handle complex, multi-parameter sample size problems in clinical trials.

Findings

Applicable to complex trial designs with multiple parameters

Demonstrated on problems with clustering, co-primary endpoints, and small samples

Can be implemented with existing statistical software

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, 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 three increasingly complicated sample size determination problems involving complex clustering structures, co-primary endpoints, and small sample considerations.