所謂的現狀資料是指只能觀察到失效時間是發生在一個隨機的監控時間之前或之後。這樣的資料大多出現在生物醫學、經濟學或是社會學等研究當中。另一方面在蒐集資料時經常受限於儀器的精準度或量測方式的限制等因素而導致共變數無法準確測量。有鑑於此， 本文探討在加法性風險模型之下影響失敗時間分佈的共變數含有測量誤差時的分析方法：利用Lin等人(1998)的看法，我們首先將加法性風險模型的問題轉換成乘法性風險模型的問題，再利用右設限資料下乘法性風險模型的特性進行共變數有測量誤差時的分析方法。

Current status data arise when the only knowledge about the failure time of interest is whether the failure occurs before or after a random monitoring time. Such data are commonly encountered in biomedicine, economic, sociology and other scientific areas. Due to the accuracy of instruments or the limitation of measurements, we may not measure the covariates precisely. This paper constructs estimation under the additive hazards model and discusses the analysis when covariates are subject to measurement error. Under certain conditions on the monitoring time and consider a certain process, the additive hazards model for the failure time implies the proportional hazards model for the special process. The analysis when measurement error presents was then derived under the proportional hazards model.

目錄
1 前言...................................................1
2 模型與推論.............................................2
  2.1 現狀資料與加法性風險模.............................2
  2.2 模型轉換...........................................3
  2.3 校正分數函數-當共變數過程與時間無關時..............5
  2.4 條件分數函數-當共變數過程為時間的線性函數時........7
3 模擬...................................................9
  3.1 當共變數過程與時間無關時...........................9
  3.2 當共變數過程為時間的線性函數時....................11
4 結論..................................................13
5 參考文獻..............................................14




表目錄
1 Zi(t) = zi , β = 0.4 . . . . . . . . . . . . . . . . 10
2 Zi(t) = zi , β = 0.6 . . . . . . . . . . . . . . . . 11
3 Zi(t) = ai + bit , β = 0.8 . . . . . . . . . . . . . 12
4 Zi(t) = ai + bit , β = 1.0 . . . . . . . . . . . . . 13

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