In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of t...In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.展开更多
To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g com...To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g computation as a reliable method of covariate adjustment.How-ever,the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest.To fill this gap,we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.展开更多
We first want to commend(Shao,2021)for a timely paper that reviews the methodological and theoretical advances in statistical inference after covariateadaptive randomisation in the last decade.The paper clearly presen...We first want to commend(Shao,2021)for a timely paper that reviews the methodological and theoretical advances in statistical inference after covariateadaptive randomisation in the last decade.The paper clearly presents the important considerations and pragmatic recommendations when analysing data obtained from covariate-adaptive randomisation,which provides principled guidelines for the practice.展开更多
文摘In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.
基金This work was supported by National Institute of Allergy and Infectious Diseases[NIAID 5 UM1 AI068617].
文摘To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes,researchers and regulatory agencies recommend using g computation as a reliable method of covariate adjustment.How-ever,the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest.To fill this gap,we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.
文摘Sample size calculations in clinical research,third edition,by Shein-Chung Chow,Jun Shao,Hansheng Wang,and Yuliya Lokhnygina,Chapman&Hall/CRC Biostatistics Series,New York,Taylor&Francis,2017,510 pp.,$99.95(hardback),ISBN:978-1-138-74098-3Sample Size Calculations in Clinical Research has its third edition appeared in 2017,written by Professors Shein-Chung Chow(Duke University),Jun Shao(University of Wisconsin-Madison),Hansheng Wang(Peking University)and Yuliya Lokhnygina(Duke University).
文摘We first want to commend(Shao,2021)for a timely paper that reviews the methodological and theoretical advances in statistical inference after covariateadaptive randomisation in the last decade.The paper clearly presents the important considerations and pragmatic recommendations when analysing data obtained from covariate-adaptive randomisation,which provides principled guidelines for the practice.
