Linear transformation models for censored data under truncation

Huan Wang (Lead / Corresponding author), Hongsheng Dai, Marialuisa Restaino, Yanchun Bao

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    Abstract

    In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both truncation and censoring. By incorporating truncation, the potential referral bias in practice is taken into account. A class of generalised estimating equations are proposed to obtain unbiased estimates of the regression parameters. Large sample properties of the proposed estimator are provided. Simulation studies under different scenarios and analyses of real-world datasets are conducted to assess the performance of the proposed estimator.

    Original languageEnglish
    Pages (from-to)42-54
    Number of pages13
    JournalJournal of Statistical Planning and Inference
    Volume193
    Early online date18 Aug 2017
    DOIs
    Publication statusPublished - Feb 2018

    Keywords

    • Bivariate survival function
    • Censoring
    • Linear transformation model
    • Survival analysis
    • Truncation

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