Comments on ‘Sample size calculation for the proportional hazards cure model’ by Songfeng Wang, Jiajia Zhang and Wenbin Lu

2015 ◽  
Vol 34 (17) ◽  
pp. 2576-2577 ◽  
Author(s):  
Jianrong Wu
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Cure Model

Authors' Reply to comments on ‘Sample size calculation for the proportional hazards cure model’

2015 ◽  
Vol 34 (17) ◽  
pp. 2578-2580
Author(s):  
Songfeng Wang ◽  
Jiajia Zhang ◽  
Wenbin Lu
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Cure Model

scholarly journals Sample size calculation for the proportional hazards cure model

2012 ◽  
Vol 31 (29) ◽  
pp. 3959-3971 ◽  
Author(s):  
Songfeng Wang ◽  
Jiajia Zhang ◽  
Wenbin Lu
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Cure Model

Sample size calculation for a proportional hazards mixture cure model with nonbinary covariates

2018 ◽  
Vol 46 (3) ◽  
pp. 468-483
Author(s):  
Yihong Zhan ◽  
Yanan Zhang ◽  
Jiajia Zhang ◽  
Bo Cai ◽  
James W. Hardin
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Cure Model ◽  
Mixture Cure Model

A unified approach to power and sample size determination for log-rank tests under proportional and nonproportional hazards

2021 ◽  
pp. 096228022098857
Author(s):  
Yongqiang Tang
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Unified Approach ◽  
Rank Tests ◽  
Trial Duration ◽  
Nonproportional Hazards ◽  
Equivalence Trials ◽  
Superiority Trials ◽  
Log Rank Tests

Log-rank tests have been widely used to compare two survival curves in biomedical research. We describe a unified approach to power and sample size calculation for the unweighted and weighted log-rank tests in superiority, noninferiority and equivalence trials. It is suitable for both time-driven and event-driven trials. A numerical algorithm is suggested. It allows flexible specification of the patient accrual distribution, baseline hazards, and proportional or nonproportional hazards patterns, and enables efficient sample size calculation when there are a range of choices for the patient accrual pattern and trial duration. A confidence interval method is proposed for the trial duration of an event-driven trial. We point out potential issues with several popular sample size formulae. Under proportional hazards, the power of a survival trial is commonly believed to be determined by the number of observed events. The belief is roughly valid for noninferiority and equivalence trials with similar survival and censoring distributions between two groups, and for superiority trials with balanced group sizes. In unbalanced superiority trials, the power depends also on other factors such as data maturity. Surprisingly, the log-rank test usually yields slightly higher power than the Wald test from the Cox model under proportional hazards in simulations. We consider various nonproportional hazards patterns induced by delayed effects, cure fractions, and/or treatment switching. Explicit power formulae are derived for the combination test that takes the maximum of two or more weighted log-rank tests to handle uncertain nonproportional hazards patterns. Numerical examples are presented for illustration.

scholarly journals NPHMC: An R-package for estimating sample size of proportional hazards mixture cure model

2014 ◽  
Vol 113 (1) ◽  
pp. 290-300 ◽  
Author(s):  
Chao Cai ◽  
Songfeng Wang ◽  
Wenbin Lu ◽  
Jiajia Zhang
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
R Package ◽  
Cure Model ◽  
Mixture Cure Model

scholarly journals Using the Geometric Average Hazard Ratio in Sample Size Calculation for Time-to-event Data With Composite Endpoints

2021 ◽  
Author(s):  
Jordi Cortés Martínez ◽  
Ronald B Geskus ◽  
KyungMann Kim ◽  
Guadalupe Gómez Melis
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Composite Endpoint ◽  
Hazard Ratio ◽  
Time To Event ◽  
Logrank Test ◽  
Geometric Average ◽  
Effect Measure ◽  
Average Hazard Ratio

Abstract Background: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphilococcus aureus related death by 12 weeks. Results: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption.Conclusions: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.

Sign in / Sign up

Export Citation Format

Share Document