Semiparametric analysis of incomplete current status outcome data under transformation models
學年 102
學期 2
出版（發表）日期 2014-06-01
作品名稱 Semiparametric analysis of incomplete current status outcome data under transformation models
作品名稱（其他語言）
著者 Wen, Chi-Chung; Chen, Yi-Hau
單位 淡江大學數學學系
出版者 Chichester: Wiley-Blackwell Publishing Ltd.
著錄名稱、卷期、頁數 Biometrics 70(2), pp.335-345
摘要 This work, motivated by an osteoporosis survey study, considers regression analysis with incompletely observed current status data. Here the current status data, including an examination time and an indicator for whether or not the event of interest has occurred by the examination time, is not observed for all subjects. Instead, a surrogate outcome subject to misclassification of the current status is available for all subjects. We focus on semiparametric regression under transformation models, including the proportional hazards and proportional odds models as special cases. Under the missing at random mechanism where the missingness of the current status outcome can depend only on the observed surrogate outcome and covariates, we propose an approach of validation likelihood based on the likelihood from the validation subsample where the data are fully observed, with adjustments of the probability of observing the current status outcome, as well as the distribution of the surrogate outcome in the validation subsample. We propose an efficient computation algorithm for implementation, and derive consistency and asymptotic normality for inference with the proposed estimator. The application to the osteoporosis survey data and simulations reveal that the validation likelihood performs well; it removes the bias from the “complete case” analysis discarding subjects with missing data, and achieves higher efficiency than the inverse probability weighting analysis.
關鍵字
語言 en
ISSN 1541-0420
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chen, Yi-Hau
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版
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