Statistical power and sample size calculations for time-to-event analysis

Abstract

ObjectiveTo provide thoracic and cardiovascular surgeons with the necessary tools for performing sample size and power calculations for studies with time-to-event outcomes. MethodsPower and sample size calculations enhance the overall quality of research studies by providing readers with assurance and insight into the number of patients included in the study. A 5-step approach is presented for performing sample size calculations in comparing groups on time-to-event endpoints. The steps are as follows: (1) identify the primary outcome of interest, (2) define size of the effect and desired power, (3) determine the appropriate statistical test, (4) perform calculations of the required sample size, and (5) write formal power and sample size statement. This approach is demonstrated with 5 clinical examples for time-to-event studies in cardiovascular surgery, featuring Cox regression, 2-sample log-rank test, 1-sample log-rank test, and competing risks analysis. ConclusionsStatistical power is an essential element for designing studies to ensure sufficient sample sizes for detecting treatment effects or group differences in time-to-event patient outcomes. Power and sample size justification not only adds statistical rigor and credibility to research manuscripts, but also provides the reader with assurance that the findings and conclusions are valid and based on a sufficient number of patients.