Analysis for Time-to-Event Data under Censoring and Truncation

Hongsheng Dai, Huan Wang

    Research output: Book/ReportBook

    3 Citations (Scopus)

    Abstract

    Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data.

    Original languageEnglish
    Place of PublicationUnited Kingdom
    PublisherAcademic Press
    Number of pages102
    Edition1
    ISBN (Electronic)9780081010082
    ISBN (Print)9780128054802
    Publication statusPublished - 26 Sep 2016

    Fingerprint

    Truncated Data
    Censoring
    Truncation
    Selection Bias
    Survival Analysis
    Survival Data
    Survival Function
    Univariate
    Left Truncation
    Survival Time
    Risk Factors
    Biased
    Interval
    Estimate

    Cite this

    Dai, H., & Wang, H. (2016). Analysis for Time-to-Event Data under Censoring and Truncation. (1 ed.) United Kingdom: Academic Press.
    Dai, Hongsheng ; Wang, Huan. / Analysis for Time-to-Event Data under Censoring and Truncation. 1 ed. United Kingdom : Academic Press, 2016. 102 p.
    @book{4d0b0085753940aeb3847d516ce910d4,
    title = "Analysis for Time-to-Event Data under Censoring and Truncation",
    abstract = "Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data.",
    author = "Hongsheng Dai and Huan Wang",
    note = "{\circledC} Academic Press 2017",
    year = "2016",
    month = "9",
    day = "26",
    language = "English",
    isbn = "9780128054802",
    publisher = "Academic Press",
    edition = "1",

    }

    Dai, H & Wang, H 2016, Analysis for Time-to-Event Data under Censoring and Truncation. 1 edn, Academic Press, United Kingdom.

    Analysis for Time-to-Event Data under Censoring and Truncation. / Dai, Hongsheng; Wang, Huan.

    1 ed. United Kingdom : Academic Press, 2016. 102 p.

    Research output: Book/ReportBook

    TY - BOOK

    T1 - Analysis for Time-to-Event Data under Censoring and Truncation

    AU - Dai, Hongsheng

    AU - Wang, Huan

    N1 - © Academic Press 2017

    PY - 2016/9/26

    Y1 - 2016/9/26

    N2 - Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data.

    AB - Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data.

    UR - https://www.elsevier.com/books/analysis-for-time-to-event-data-under-censoring-and-truncation/dai/978-0-12-805480-2

    UR - http://www.scopus.com/inward/record.url?scp=85021868363&partnerID=8YFLogxK

    M3 - Book

    AN - SCOPUS:85021868363

    SN - 9780128054802

    BT - Analysis for Time-to-Event Data under Censoring and Truncation

    PB - Academic Press

    CY - United Kingdom

    ER -

    Dai H, Wang H. Analysis for Time-to-Event Data under Censoring and Truncation. 1 ed. United Kingdom: Academic Press, 2016. 102 p.