在過去30年中，多數文獻針對右設限事件時間作分析討論。由於醫學研究的發展， 區間設限資料在臨床試驗研究上被廣泛收集。在這樣的試驗中，每位研究對象的病人都是週期性的被檢驗或觀察，因此我們可以視事件時間為某段確定的時間，稱之為區間設限時間。考慮多重事件時間的關係並提供彈性的方法為一新課題。 此外關於如何選取適當的迴歸模型來分析事件時間的方法是很重要的。Chen, and Tong (2010) 提出轉換模型來處理右設限事件時間，此法提供一彈性模型避免直接選取某迴歸模型的問題。在這一個計畫中，我們將探討轉換模型於多重區間設限資料之應用。 A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. In these cases, each study subject is examined or observed periodically, thus the observed failure time falls into a certain interval. In the analysis of multivariate interval-censored failure time data include the estimating the correlation among failure times. Additional question for the regression analysis of failure time data is how one can choose an appropriate model, which include the proportional hazards and proportional odds model as special cases. Chen, and Tong (2010) proposed a transformation models with censored data may release the problem of selecting an appropriate model. In this project, we discuss how to fit the transformation model to multivariate interval-censored failure time data. For estimation, an Expectation Maximization (EM) algorithm is developed and simulation study is presented to show that the proposed method performs well with a finite sample and is easy to use in practice.