Download 交通大學 - 國立清華大學統計學研究所

清華大學、交通大學
統 計 學 研 究 所
專 題 演 講
題
目: Event History Analysis with Non-Susceptibility and
Heteroscedasticity for Left-Truncated and General
Interval-Censored Data
主講人: 陳珍信 教授
(中央研究院統計科學研究所
台灣大學公共衛生學院流行病學研究所)
時
間: 98年10月02日（星期五）上午 10：40 - 11：30
（上午10：20- 10：40茶會於統計所821室舉行）
地
點:
清大綜合三館 837 室
Abstract
In population-based studies and genomic medicine research, it is
important to delineate genetic, environmental effects and their interactions on
specific complex diseases/disorders. Statistical methods for analyzing this
kind of data have been developing. In most of the cases, outcomes of the
disease/disorder under study are treated as either a dichotomous status or a
continuous measurement of age at onset. The former approaches do not
consider the probability of later onset for observed non-cases, while the latter
approaches ignore the disease non-susceptibility in the non-cases, who did
not inherit the related genes or were never exposed to deleterious
environmental factors. These two kinds of approaches may render misleading
interpretations.
In this presentation we briefly review the conventional survival analysis
which implicitly assumes all the study subjects are susceptible to the event.
To analyze the non-susceptibility, recent studies proposed mixture regression
models to investigate the respective risk factors for the probability of disease
susceptibility and the age-at-onset distribution simultaneously for right
censored data. In epidemiological studies with longitudinal data, we often
encounter left, interval and right censored data, as well as possible left
truncated data due to different entry ages of recruited healthy subjects. The
resultant survival curves may emerge plateaus on right tails and multiple
crossings over covariate strata. To tackle these issues of incomplete data, we
present the mixture regression model combining the logistic model with the
accelerated failure time location-scale model. We also apply the model to
analyze age-at-onset studies in a few epidemiological research projects in
Taiwan. A three-component mixture regression model is extended to study
the willingness-to-pay for non-market health services using double-bound
dichotomous choice contingent valuation surveys.
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