Hypothesis Testing with Paired Partly Interval Censored Data  

作　　者：Ding-jiao CAI Bo LU Xing-wei TONG 

机构地区：[1]School of Mathematics and Information Science,Henan University of Economics and Law,Zhengzhou 450046,China [2]Division of Biostatistics,College of Public Health,Ohio State University,Columbus,Ohio,43210,U.S.A [3]School of Statistics,Beijing Normal University,Beijing 100875,China

出　　处：《Acta Mathematicae Applicatae Sinica》2019年第3期541-548,共8页应用数学学报（英文版）

基　　金：supported by grant 1R01 HS024263-01 from the Agency of Healthcare Research and Quality of the U.S. Department of Health and Human Services

摘　　要：Partly interval censored data frequently occur in many areas including clinical trials,epidemiology research,and medical follow-up studies.When data come from observational studies,we need to carefully adjust for the confounding bias in order to estimate the true treatment effect.Pair matching designs are popular for removing confounding bias without parametric assumptions.With time-to-event outcomes,there are some literature for hypothesis testing with paired right censored data,but not for interval censored data.O’Brien and Fleming extended the Prentice Wilcoxon test to right censored paired data by making use of the PrenticeWilcoxon scores.Akritas proposed the Akritas test and established its asymptotic properties.We extend Akritas test to partly interval censored data.We estimate the survival distribution function by nonparametric maximum likelihood estimation(NPMLE),and prove the asymptotic validity of the new test.To improve our test under small sample size or extreme distributions,we also propose a modified version using the rank of the score difference.Simulation results indicate that our proposed methods have very good performance.Partly interval censored data frequently occur in many areas including clinical trials, epidemiology research, and medical follow-up studies. When data come from observational studies, we need to carefully adjust for the confounding bias in order to estimate the true treatment effect. Pair matching designs are popular for removing confounding bias without parametric assumptions. With time-to-event outcomes, there are some literature for hypothesis testing with paired right censored data, but not for interval censored data. O’Brien and Fleming extended the Prentice Wilcoxon test to right censored paired data by making use of the PrenticeWilcoxon scores. Akritas proposed the Akritas test and established its asymptotic properties. We extend Akritas test to partly interval censored data. We estimate the survival distribution function by nonparametric maximum likelihood estimation(NPMLE), and prove the asymptotic validity of the new test. To improve our test under small sample size or extreme distributions, we also propose a modified version using the rank of the score difference. Simulation results indicate that our proposed methods have very good performance.

关 键 词：partly INTERVAL censored data NONPARAMETRIC MAXIMUM LIKELIHOOD estimation Wilcoxon SIGNED RANK test 

分 类 号：O1[理学—数学]