【3/31專題演講】曾信嘉教授-重複間隔時間資料在分量迴歸下的Buckley-James估計法
＊主講人：曾信嘉副教授<國立嘉義大學 農藝學系>
＊題目：重複間隔時間資料在分量迴歸下的Buckley-James估計法
(Buckley-James-Type Estimation of Quantile Regression with Recurrent Gap Time Data)
＊時間：105年3月31日(四)10：10-11：40
＊摘要：
In longitudinal studies an individual may potentially undergo a series of repeated recurrence events. The gap times, which are referred to as the times between successive recurrent events, are typically the outcome variables of interest. Various regression models have been developed in order to evaluate covariate effects on gap times based on recurrence event data. The proportional hazards model, additive hazards model, and the accelerated failure time model are all notable examples. Quantile regression is a useful alternative to the aforementioned models for survival analysis since it can provide great flexibility to assess covariate effects on the entire distribution of the gap time. In order to analyze recurrence gap time data, we must overcome the problem of the last gap time subjected to induced informative censoring, when numbers of recurrent events exceed one time. In this study, we adopt the Buckley-James-type estimation method in order to construct a weighted estimation equation for regression coefficients under the quantile model, and develop an iterative procedure to obtain the estimates. We use extensive simulation studies to evaluate the finite-sample performance of the proposed estimator. Finally, analysis of bladder cancer data is presented as an illustration of our proposed methodology.
＊題目：重複間隔時間資料在分量迴歸下的Buckley-James估計法
(Buckley-James-Type Estimation of Quantile Regression with Recurrent Gap Time Data)
＊時間：105年3月31日(四)10：10-11：40
＊摘要：
In longitudinal studies an individual may potentially undergo a series of repeated recurrence events. The gap times, which are referred to as the times between successive recurrent events, are typically the outcome variables of interest. Various regression models have been developed in order to evaluate covariate effects on gap times based on recurrence event data. The proportional hazards model, additive hazards model, and the accelerated failure time model are all notable examples. Quantile regression is a useful alternative to the aforementioned models for survival analysis since it can provide great flexibility to assess covariate effects on the entire distribution of the gap time. In order to analyze recurrence gap time data, we must overcome the problem of the last gap time subjected to induced informative censoring, when numbers of recurrent events exceed one time. In this study, we adopt the Buckley-James-type estimation method in order to construct a weighted estimation equation for regression coefficients under the quantile model, and develop an iterative procedure to obtain the estimates. We use extensive simulation studies to evaluate the finite-sample performance of the proposed estimator. Finally, analysis of bladder cancer data is presented as an illustration of our proposed methodology.