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Analysis of linear transformation models with covariate measurement error and interval censoring
Author(s) -
Mandal Soutrik,
Wang Suojin,
Sinha Samiran
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8323
Subject(s) - covariate , censoring (clinical trials) , proportional hazards model , statistics , semiparametric model , semiparametric regression , imputation (statistics) , mathematics , econometrics , regression analysis , computer science , estimator , missing data
Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time‐to‐event when the observed time‐to‐event is interval‐censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval‐censored data, flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time‐to‐event data with different models, in this paper, a general approach is proposed for fitting the semiparametric linear transformation model to interval‐censored data when a covariate is measured with error. The semiparametric linear transformation model is a broad class of models that includes the proportional hazard model and the proportional odds model as special cases. The proposed method relies on a set of estimating equations to estimate the regression parameters and the infinite‐dimensional parameter. For handling interval censoring and covariate measurement error, a flexible imputation technique is used. Finite sample performance of the proposed method is judged via simulation studies. Finally, the suggested method is applied to analyze a real data set from an AIDS clinical trial.