Logistic-AFT location-scale mixture regression models with nonsusceptibility for left-truncated and general interval-censored data

Authors

  • Chen-Hsin Chen,

    Corresponding author
    1. Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan
    • Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
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  • Yuh-Chyuan Tsay,

    1. Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
    2. Division of Biometry, Institute of Agronomy, National Taiwan University, Taipei 10764, Taiwan
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  • Ya-Chi Wu,

    1. Division of Clinical Sciences, Center for Drug Evaluation, Taipei 11557, Taiwan
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  • Cheng-Fang Horng

    1. Biostatistics Section, Clinical Protocol Office, Koo Foundation Sun Yat-Sen Cancer Center, Taipei 11259, Taiwan
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Correspondence to: Chen-Hsin Chen, Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.

E-mail: chchen@stat.sinica.edu.tw

Abstract

In conventional survival analysis there is an underlying assumption that all study subjects are susceptible to the event. In general, this assumption does not adequately hold when investigating the time to an event other than death. Owing to genetic and/or environmental etiology, study subjects may not be susceptible to the disease. Analyzing nonsusceptibility has become an important topic in biomedical, epidemiological, and sociological research, with recent statistical studies proposing several mixture models for right-censored data in regression analysis. In longitudinal studies, we often encounter left, interval, and right-censored data because of incomplete observations of the time endpoint, as well as possibly left-truncated data arising from the dissimilar entry ages of recruited healthy subjects. To analyze these kinds of incomplete data while accounting for nonsusceptibility and possible crossing hazards in the framework of mixture regression models, we utilize a logistic regression model to specify the probability of susceptibility, and a generalized gamma distribution, or a log-logistic distribution, in the accelerated failure time location-scale regression model to formulate the time to the event. Relative times of the conditional event time distribution for susceptible subjects are extended in the accelerated failure time location-scale submodel. We also construct graphical goodness-of-fit procedures on the basis of the Turnbull–Frydman estimator and newly proposed residuals. Simulation studies were conducted to demonstrate the validity of the proposed estimation procedure. The mixture regression models are illustrated with alcohol abuse data from the Taiwan Aboriginal Study Project and hypertriglyceridemia data from the Cardiovascular Disease Risk Factor Two-township Study in Taiwan. Copyright © 2013 John Wiley & Sons, Ltd.

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