Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome

Patrick Royston ()
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Patrick Royston: University College London

Stata Journal, 2018, vol. 18, issue 1, 3-21

Abstract: Randomized controlled trials with a time-to-event outcome are usually designed and analyzed assuming proportional hazards (PH) of the treatment effect. The sample-size calculation is based on a log-rank test or the nearly identical Cox test, henceforth called the Cox/log-rank test. Nonproportional hazards (non-PH) has become more common in trials and is recognized as a potential threat to interpreting the trial treatment effect and the power of the log-rank test—hence to the success of the trial. To address the issue, in 2016, Royston and Parmar (BMC Medical Research Methodology 16: 16) proposed a “combined test” of the global null hypothesis of identical survival curves in each trial arm. The Cox/log- rank test is combined with a new test derived from the maximal standardized difference in restricted mean survival time (RMST) between the trial arms. The test statistic is based on evaluations of the between-arm difference in RMST over several preselected time points. The combined test involves the minimum p-value across the Cox/log-rank and RMST-based tests, appropriately standardized to have the correct distribution under the global null hypothesis. In this article, I introduce a new command, power ct, that uses simulation to implement power and sample-size calculations for the combined test. power ct supports designs with PH or non-PH of the treatment effect. I provide examples in which the power of the combined test is compared with that of the Cox/log-rank test under PH and non-PH scenarios. I conclude by offering guidance for sample-size calculations in time-to-event trials to allow for possible non-PH.

Keywords: power_ct; randomized controlled trial; time-to-event outcome; restricted mean survival time; log-rank test; Cox test; combined test; treatment effect; hypothesis testing; flexible parametric model (search for similar items in EconPapers)
Date: 2018
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