Estimation of a Concordance Probability for Doubly Censored Time-to-Event Data

Kenichi Hayashi, Yasutaka Shimizu

    Research output: Contribution to journalArticle

    Abstract

    Evaluating the relationship between a response variable and explanatory variables is important to establish better statistical models. Concordance probability is one measure of this relationship and is often used in biomedical research. Concordance probability can be seen as an extension of the area under the receiver operating characteristic curve. In this study, we propose estimators of concordance probability for time-to-event data subject to double censoring. A doubly censored time-to-event response is observed when either left or right censoring may occur. In the presence of double censoring, existing estimators of concordance probability lack desirable properties such as consistency and asymptotic normality. The proposed estimators consist of estimators of the left-censoring and the right-censoring distributions as a weight for each pair of cases, and reduce to the existing estimators in special cases. We show the statistical properties of the proposed estimators and evaluate their performance via numerical experiments.

    Original languageEnglish
    Pages (from-to)1-22
    Number of pages22
    JournalStatistics in Biosciences
    DOIs
    Publication statusAccepted/In press - 2018 Mar 22

    Fingerprint

    Concordance
    Estimator
    Left Censoring
    Right Censoring
    Censoring
    Statistical Models
    ROC Curve
    Biomedical Research
    Receiver Operating Characteristic Curve
    Asymptotic Normality
    Weights and Measures
    Statistical property
    Statistical Model
    Numerical Experiment
    Evaluate
    Experiments

    Keywords

    • Concordance probability
    • Doubly censored data
    • Time-to-event response

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry, Genetics and Molecular Biology (miscellaneous)

    Cite this

    Estimation of a Concordance Probability for Doubly Censored Time-to-Event Data. / Hayashi, Kenichi; Shimizu, Yasutaka.

    In: Statistics in Biosciences, 22.03.2018, p. 1-22.

    Research output: Contribution to journalArticle

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