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題 名 | Corrected Score Estimator for Joint Modeling of Longitudinal and Failure Time Data |
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作 者 | Wang, C. Y.; | 書刊名 | Statistica Sinica |
卷 期 | 16:1 民95.01 |
頁 次 | 頁235-253 |
分類號 | 319.534 |
關鍵詞 | Empirical process; Estimating equation; Measurement error; Proportional hazards; Random effects; |
語 文 | 英文(English) |
英文摘要 | We consider Cox proportional hazards regression when longitudinal measurements are available. In some applications, one major goal is to estimate the effect of the underlying change of the longitudinal measurements on survival. One general approach considers regression analysis when some covariate variables are the underlying regression coefficients of another random effects model. For each subject, the covaritate variables to the primary regression model are not observed, but can be setgiamted from the observed longitudinal measurements. This set-up is often called joint modeling in the literature, but it can be treated as two-stage modeling. In this paper, a corrected score estimator is investigated. Comparisons are made with a naïve estimator, a regression calibration estimator, a risk set regression calibration estimator, and a conditional score estimator. Similar to the conditional score estimator, the corrected score estimator does not need the assumption of an underlying distribution of the random effects for each subject. Under some regularity conditions, the proposed corrected score estimator is shown to be consistent and asymptotically normally distributed. Simulation results under various random effects distributions are presented. |
本系統中英文摘要資訊取自各篇刊載內容。