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学术报告


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Speaker:

Prof. Suojin Wang,Texas A&M University

Inviter: 王启华 研究员
Title:
Semiparametric Analysis of Linear Transformation Models with Covariate Measurement Error and Interval Censoring
Time & Venue:

2019.5.29 16:00 N613

Abstract:

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, 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.

Affiliation:  

学术报告中国科学院数学与系统科学研究院日搏官网

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