Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Centre for Primary Care & Public Health, Queen Mary University of London, London, UK.
Stat Methods Med Res. 2021 Mar;30(3):799-815. doi: 10.1177/0962280220975790. Epub 2020 Dec 2.
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.
模拟提供了一种简单灵活的方法来估计临床试验的功效，当没有分析公式可用时。然而，由于模拟的计算负担，它的应用仅限于最简单的样本量确定问题，通常是最小化一个单一参数（总体样本量），以满足功效高于目标水平。我们描述了一种通用框架，用于解决具有多个设计参数的基于模拟的样本量确定问题，并对多个冲突的标准进行优化和最小化。该方法基于一种已建立的全局优化算法，广泛应用于计算机实验的设计和分析中，使用非参数回归模型作为真实潜在功效函数的近似。该方法具有灵活性，可以用于几乎任何可以使用模拟估计功效的问题，并且可以使用现有的统计软件包来实现。我们将其应用于一个涉及复杂聚类结构、两个主要终点和小样本考虑的样本量确定问题进行说明。
