| Summary: | 碩士 === 國立臺灣大學 === 流行病學研究所 === 97 === In follow-up studies, bivariate event data are often encountered when subjects may experience two events with different or the same types. In analyzing bivariate event data, two types of time scale are considered in the literature: the times to events and the times between adjacent events. When the occurrence of during follow-up two events is in a chronological order, the gap time, defined as the time between adjacent events, is often of interest in the study. In the literature, measures of association between bivariate event times have been studied, but not yet for two serial gap times. In general, standard techniques for analyzing the times to events are inappropriate for analyzing the second gap time due to the presence of induced informative censoring. In this paper, Kendall''s tau is considered as a measure of association between gap times. Nonparametric estimation of Kendall’s tau for two serial gap times is proposed in which inverse probability of censoring weights is used to accommodate the bias from induced informative censoring. Asymptotic normality of the proposed estimator of Kendall''s tau is obtained by employing the theory of U statistics along with martingale techniques. Finally, the performance of the proposed method is illustrated by a simulation study.
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