右設限失效時間資料的分析方法在過去三十年間已發展的非常完善，隨著發展生物醫學研究，在醫療後續研究中一般所遇到的資料為區間設限資料，例如我們對病患定期調查或觀察，因此真實的失效時間也許未能準確觀察，我們只知道會在某些時間區間當中。 另一個在分析上所遇到的問題為多變量區間設限失效時間資料，我們所感興趣的失效時間不只一個，且每個失效時間中有相關性的存在。本篇論文考慮使用脆弱模型(frailty model)分析多變量區間設限失效時間資料，使用EM演算法(EM algorithm)估計其迴歸參數，並透過模擬驗證之。 A voluminous literature on right-censored failure time data has been developed very well in the pust 30 years. Due to advances in biomedical research, interval censoring has become common in medical follow-up studies. For example, each study subject is observed periodically, thus the observed failure time falls into a time period. Another problems is multivariate interval-censored failure time data. Multivariate failure time data occur when one is interested in several related failure times. This thesis considers regression analysis of multivariate interval-censored failure time data using by the random effect approach. For estimation, an Expectation Maximization (EM) algorithm is developed and simulation studies suggest that the frailty model approach.
