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.

The Finkelstein and Schoenfeld (FS) test is a popular generalized pairwise comparison approach to analyze prioritized composite endpoints (e.g., components are assessed in order of clinical importance). Power and sample size estimation for the FS test, however, are generally done via simulation studies. This simulation approach can be extremely computationally burdensome, compounded by an increasing number of composite endpoints and with increasing sample size. We propose an analytic solution to calculate power and sample size for commonly encountered two-component hierarchical composite endpoints. The power formulas are derived assuming underlying distributions in each of the component outcomes on the population level, which provide a computationally efficient and practical alternative to the standard simulation approach. The proposed analytic approach is extended to derive conditional power formulas, which are used in combination with the promising zone methodology to perform sample size re-estimation in the setting of adaptive clinical trials. Prioritized composite endpoints with more than two components are also investigated. Extensive Monte Carlo simulation studies were conducted to demonstrate that the performance of the proposed analytic approach is consistent with that of the standard simulation approach. We also demonstrate through simulations that the proposed methodology possesses generally desirable objective properties including robustness to mis-specified underlying distributional assumptions. We illustrate our proposed methods through application of the proposed formulas by calculating power and sample size for the Transthyretin Amyloidosis Cardiomyopathy Clinical Trial (ATTR-ACT) and the EMPULSE trial for empagliozin treatment of acute heart failure.