Sample size calculation for cluster randomization trials with a time‐to‐event endpoint

2020 ◽  
Vol 39 (25) ◽  
pp. 3608-3623
Author(s):  
Jianghao Li ◽  
Sin‐Ho Jung
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Cluster Randomization ◽  
Time To Event

scholarly journals Power and Sample-Size Analysis for the Royston–Parmar Combined Test in Clinical Trials with a Time-to-Event Outcome: Correction and Program Update

2018 ◽  
Vol 18 (4) ◽  
pp. 995-996 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Estimation Procedure ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Size Analysis ◽  
Size Estimation ◽  
Probit Regression ◽  
Time To Event ◽  
Combined Test

The changes made to Royston (2018) and to power_ct are i) in section 2.4 ( Sample-size calculation for the combined test), to replace ordinary least-squares (OLS) regression using regress with grouped probit regression using glm; ii) in section 4 ( Examples), to revisit the worked examples of sample-size estimation in light of the revised estimation procedure; and iii) to update the help file entry for the option n( numlist). The updated software is version 1.2.0.

scholarly journals A Combined Test for a Generalized Treatment Effect in Clinical Trials with a Time-to-event Outcome

2017 ◽  
Vol 17 (2) ◽  
pp. 405-421 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Rank Test ◽  
P Value ◽  
Time To Event ◽  
Test Statistic ◽  
Potential Threat ◽  
Log Rank Test ◽  
Combined Test

Most randomized controlled trials with a time-to-event outcome are designed and analyzed assuming proportional hazards of the treatment effect. The sample-size calculation is based on a log-rank test or the equivalent Cox test. Nonproportional hazards are seen increasingly in trials and are recognized as a potential threat to the power of the log-rank test. To address the issue, Royston and Parmar (2016, BMC Medical Research Methodology 16: 16) devised a new “combined test” of the global null hypothesis of identical survival curves in each trial arm. The test, which combines the conventional Cox test with a new formulation, is based on the maximal standardized difference in restricted mean survival time (RMST) between the arms. The test statistic is based on evaluations of RMST over several preselected time points. The combined test involves the minimum p-value across the Cox and RMST-based tests, appropriately standardized to have the correct null distribution. In this article, I outline the combined test and introduce a command, stctest, that implements the combined test. I point the way to additional tools currently under development for power and sample-size calculation for the combined test.

Sign in / Sign up

Export Citation Format

Share Document