The selection of the best response variables in a clinical trial is often not straightforward; the primary endpoint of a trial should be clinically relevant, directly related to the primary objective of the trial, and with favorable efficiency to detect the treatment benefit with a reasonable sample size and duration of the trial. With the recent success in the management of heart failure, the mortality rate has dropped significantly compared to two decades ago, and patients with heart failure have high rates of hospitalization and morbid complications along with multiple symptoms and severe limitations in daily activities. Although mortality still remains important as a measure of the clinically relevant benefit and the safety of the intervention, with the low event rate of mortality, it requires large and longer clinical trials to detect treatment benefit of new intervention using mortality as the sole primary endpoint. Thus most heart failure trials use the combined endpoint of death and a second efficacy outcome, such as hospitalizations. This is often analyzed with time-to-first-event survival analysis which ignores possible subsequent hospitalization events and treating the death and first hospitalization equally in the importance and hierarchy of clinical relevance. Accounting for the recurrent events or subsequent death after the hospitalization(s) provides more detailed information on the disease-control process and treatment benefit. In this dissertation we propose a hierarchical endpoint with death in the higher priority and number of hospitalization events in the lower priority as primary endpoint to assess experimental treatment benefit versus a control using a non-parametric generalized Gehan-Wilcoxon test. In addition to the hierarchical endpoint, we also evaluated assessment of experimental treatment benefit on recurrent events with a multi-state model using extended stratified Cox model, considering the multi-states in which patients might transition during the study. We compared the false positive rate and power of the above mentioned methods with the composite endpoint approach and recurrent event endpoint approach analyzed using Andersen-Gill, WLW, and PWP models in simulation studies. Finally we applied all evaluated procedures to the Digitalis Investigation Group (DIG) trial.

對於生存時間的資料而言，通常我們所想要研究瞭解的是，至少存活到某特定時間點的機率，而這個機率亦即生存分析中的生存函數(survival function)。當有兩個不同的母體存在時，為了要知道這兩個母體的生存函數是否相同，在統計方法上，我們將進行一些檢定，常用的有Gehan-Wilcoxon和Cox-Mantel之兩樣本檢定，後來又有修飾型的Kolmogorov-Smirnov檢定。但是，前兩種檢定方法，只對此兩組生存函數呈現某特殊型式時，具有好的檢定力。因此，透過一些實證的研究，將上述檢定方法做有系統的整理，進而發展出一套簡單又有效率的檢定程序。再者，若檢定得此兩個母體之生存函數不相等時，如何利用Bootstrap方法，進一步對兩組生存函數之特定生存機率點或生存時間點所分別對應之生存時間或生存機率差距做推論與比較，本文將有詳細她說明；以提供研究人員更多有效的資訊，不再僅止於檢定虛無假設是否拒絕而已。最後，我們又藉由推廣上述Bootstrap方法，將其運用到檢定方法上，而另外發展出一種新的兩母體生存函數之檢定方法。 / When two different populations exist, we will take some tests by Cehan-Wilcoxon, Cox-Mantel or Modified Kolmogorov- Smirnov in satistical way. Therefore we develope a simple and efficient test process from arranging above test ways system- atically through some real study. How to use Bootstrap way to infer the difference of survival time or survival probability of specular point. We infer Bootstrap way on test work and then develope a new two populations survival function test way.