具共變量測量誤差之右設限數資料已被廣泛的研究，但對混合區間設限數據卻很少 研究。研究動機為AIDS Clinical Trial Group (ACTG) 175 的研究，其中AIDS 的發生時 間僅在斷續的回診時間才作觀察，而在進行治療前的共變量:基線CD4 數存有量測誤差。 因此，我們對於具共變量測量誤差之混合區間設限資料，將提出一個結構模式的「半母 數最大概似法」和一個機能模式的「條件分數法」來分析。當可以正確模式真實卻易有 誤差之共變量的分布時，結構模式的方法是較有效的，而機能模式的方法無需模式真實 共變量的分布，因此會較穩健。我們的目的為建立所提估計之漸進性質和計算法則。我 們將藉由模擬和實例分析來評估和敘述統計方法。 Covariate measurement error problems have been extensively studied in the context of right-censored data but less so for mixed case interval-censored data. Motivated by the AIDS Clinical Trial Group (ACTG) 175 study, where the occurrence time of AIDS was examined only at intermittent clinic visits and the baseline covariate CD4 count was measured with error, we will describe a structural modeling method (semiparametric maximum likelihood approach) and a functional modeling method (conditional score approach) for analyzing mixed case interval-censored data with mismeasured covariates. The structural modeling method is more efficient when the distribution of the true covariate is specified correctly, while the functional modeling method is more robust due to it makes no distribution assumption on the true covariate. Our goal is to establish the asymptotic properties and computation methods of the proposed estimators. Besides, we will evaluate the methods through simulation studies and illustrate them with AIDS data.
