eprintid: 10046121
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/04/61/21
datestamp: 2018-04-05 11:50:38
lastmod: 2021-12-13 23:48:22
status_changed: 2018-04-05 11:50:38
type: article
metadata_visibility: show
creators_name: Royston, P
title: Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome
ispublished: pub
divisions: UCL
divisions: B02
divisions: D65
divisions: J38
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
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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/logrank 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.
date: 2018
date_type: published
publisher: StataCorp
official_url: https://www.stata-journal.com/sj18-1.html
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1546994
lyricists_name: Royston, John
lyricists_id: JPROY48
actors_name: Royston, John
actors_name: Allington-Smith, Dominic
actors_id: JPROY48
actors_id: DAALL44
actors_role: owner
actors_role: impersonator
full_text_status: public
publication: Stata Journal
volume: 18
number: 1
pagerange: 3-21
issn: 1536-867X
citation:        Royston, P;      (2018)    Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome.                   Stata Journal , 18  (1)   pp. 3-21.          Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10046121/1/Royston_sjart_st0510.pdf