Show simple item record

dc.contributor.authorYang, Yijunen_US
dc.date.accessioned2016-04-08T17:34:52Z
dc.date.available2016-04-08T17:34:52Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/15649
dc.description.abstractThe 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.en_US
dc.language.isoen_US
dc.subjectBiostatisticsen_US
dc.subjectComposite endpointen_US
dc.subjectCoxen_US
dc.subjectGehan-Wilcoxonen_US
dc.subjectHierarchical endpointen_US
dc.subjectRecurrent eventen_US
dc.titleEvaluating multiple endpoints in heart failure clinical trialsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-03-12T07:14:03Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBiostatisticsen_US
etd.degree.grantorBoston Universityen_US


This item appears in the following Collection(s)

Show simple item record