Chen, Ding-Geng (Din)Ho, Shuyen2018-02-232017-05Ding-Geng (Din) Chen & Shuyen Ho (2017) From statistical power to statistical assurance: It's time for a paradigm change in clinical trial design, Communications in Statistics -Simulation and Computation, 46:10, 7957-7971, DOI: 10.1080/03610918.2016.1259476.0361-0918 (print)1532-4141 (online)10.1080/03610918.2016.1259476http://hdl.handle.net/2263/64065A well-designed clinical trial requires an appropriate sample size with adequate statistical power to address trial objectives. The statistical power is traditionally defined as the probability of rejecting the null hypothesis with a pre-specified true clinical treatment effect. This power is a conditional probability conditioned on the true but actually unknown effect. In practice, however, this true effect is never a fixed value. Thus, we discuss a newly proposed alternative to this conventional statistical power: statistical assurance, defined as the unconditional probability of rejecting the null hypothesis. This kind of assurance can then be obtained as an expected power where the expectation is based on the prior probability distribution of the unknown treatment effect, which leads to the Bayesian paradigm. In this article, we outline the transition from conventional statistical power to the newly developed assurance and discuss the computations of assurance using Monte Carlo simulation-based approach.en© 2017 Taylor & Francis Group, LLC. This is an electronic version of an article published in Communications in Statistics : Simulation and Computation, vol. 46, no. 10, pp. 7957-7971, 2017. doi : 10.1080/03610918.2016.1259476. Communications in Statistics : Simulation and Computation is available online at : http://www.tandfonline.comloi/lssp20.AssuranceBayesian prior distributionConditional probabilityUnconditional probabilityMonte Carlo simulationSample size determinationStatistical powerPostprint Article