This paper discusses multivariate interval-censored failure time data observed when several correlated survival times of interest exist and only interval censoring is available for each survival time. Such data occur in many fields, for instance, studies of the development of physical symptoms or diseases in several organ systems. A marginal inference approach was used to create a linear transformation model and applied to bivariate interval-censored data arising from a diabetic retinopathy study and an AIDS study. The results of simulation studies that were conducted to evaluate the performance of the presented approach suggest that it performs well. 在過去30年中，多數文獻針對右設限事件時間做分析討論。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。進而，考慮多重事件時間的關係並提供彈性的方法為一新課題。
在本篇文章中，我們將建構邊際概似函數用於多重區間設限轉換模型，求模式中的參數估計方程式，再由此估計方程式求參數估計量。接著利用模擬的方式來驗證參數估計量的一致性。最後將此推廣方法應用於實際的糖尿病與愛滋病資料。
Relation:
The Canadian Journal of Statistics 41(2), p.275–290
