A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates  

作　　者：FENG Fan ZHAO Shishun LI Shuwei SUN Jianguo 

机构地区：[1]School of Economics and Statistics,Guangzhou University,Guangzhou 510006,China [2]School of Mathematics,Jilin University,Changchun 130021,China [3]Department of Statistics,University of Missouri,MO 6521l,USA

出　　处：《Journal of Systems Science & Complexity》2024年第6期2721-2737,共17页系统科学与复杂性学报（英文版）

基　　金：supported by the National Statistical Science Research Project under Grant No.2022LY041;the Nature Science Foundation of Guangdong Province of China under Grant No.2021A1515010044;the National Nature Science Foundation of China under Grant No.12071176。

摘　　要：Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys.In addition,some covariates may be subject to measurement errors due to the instrumental contamination,biological variation or other reasons.For the analysis of interval-censored data with mis-measured covariates,the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate,which both have obvious limitations.To overcome these shortcomings,the authors propose a simulation-extrapolation estimation procedure under a general class of transformation models.The resulting estimators are shown to be consistent and asymptotically normal.The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in practice.In particular,the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account.Finally,the proposed method is applied to a real data set on hypobaric decompression sickness.

关 键 词：Interval censoring measurement error regression analysis semiparametric model SIMEX 

分 类 号：O212.1[理学—概率论与数理统计]