Sample size calculation for clinical trials analyzed with the meta‐analytic‐predictive approach

• Published in: Research synthesis methods Vol. 14; no. 3; pp. 396 - 413
• Main Authors: Qi, Hongchao, Rizopoulos, Dimitris, Rosmalen, Joost
• Format: Journal Article
• Language: English
• Published: England Wiley 01.05.2023; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian Statistics; Clinical trials; Computation; Computer Simulation; dynamic borrowing; Humans; Mathematical analysis; Meta Analysis; meta‐analytic‐predictive; Models, Statistical; Monte Carlo Method; Monte Carlo Methods; Parameters; Prediction; Randomized Controlled Trials; Research Design; Sample Size; sample size calculation; Statistical power; Test Validity; sample size calculation; Bayesian statistics; dynamic borrowing; meta-analytic-predictive
• ISSN: 1759-2879; 1759-2887; 1759-2887
• DOI: 10.1002/jrsm.1618

The meta‐analytic‐predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In previous applications of the MAP approach, the prior effective sample size (ESS) acted as a metric to quantify the number of subjects the historical information is worth. However, the validity of using the prior ESS in sample size calculation (i.e., reducing the number of randomized controls by the derived prior ESS) is questionable, because different approaches may yield different values for prior ESS. In this work, we propose a straightforward Monte Carlo approach to calculate the sample size that achieves the desired power in the new trial given available historical controls. To make full use of the available historical information to simulate the new trial data, the control parameters are not taken as a point estimate but sampled from the MAP prior. These sampled control parameters and the MAP prior based on the historical data are then used to derive the statistical power for the treatment effect and the resulting required sample size. The proposed sample size calculation approach is illustrated with real‐life data sets with different outcomes from three studies. The results show that this approach to calculating the required sample size for the MAP analysis is straightforward and generic.