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

Objective: To provide thoracic and cardiovascular surgeons with the necessary tools for performing sample size and power calculations for studies with time-to-event outcomes.Methods: Power 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.Conclusions: Statistical 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.

Automated summary

This article presents a method for performing sample size and power calculations in studies with time-to-event outcomes, and demonstrates the steps and statistical methods with clinical examples. Statistical power is essential for ensuring sufficient sample sizes in studies to detect treatment effects or group differences.