Biostatistics Seminar Series – Ying Lu, Ph.D.
Ying Lu, Ph.D.
Professor, Department of Biomedical Data Science, School of Medicine, Stanford University
“A Composite Endpoint Approach to Compare Treatments Based on Patient Preferences of Multivariate Outcomes”
Abstract:
Evaluation of medical products needs to consider its totality of evidence for benefits and harms that are measured through multiple relevant endpoints. The importance of these endpoints can vary for different clinical settings, clinicians, and patients. Evans and Follmann (2016) advocated the use the desirability of outcome ranking (DOOR) to integrate multiple outcomes according to their importance for the design and analysis of clinical trials. Wang and Chen (2019) proposed testing procedures for trend in benefit-risk analysis based on the importance of multiple outcomes. In these approaches, the priority levels can be determined by clinicians or based on survey of patients. In this paper, we proposed a stratified randomization design and a composite win ratio endpoint (Pocock et al. 2012) to evaluate the treatment benefits based on patient individually reported outcomes and importance. We introduce motivation examples to illustrate importance of patient heterogeneity in endpoint importance, discuss mathematical properties and difference of our proposed approach with others, and demonstrate the differences and advantages via design examples. We will also discuss our experience using this approach in the development of a shared decision making trial for AFib stroke prevention.
This is a joint work with Drs. Qian Zhao, Shiyan Yan, Lu Tian, Min M. Tu, and Jie Chen.
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Bio3 Seminar Series sponsored by Department of Biostatistics, Bioinformatics & Biomathematics (DBBB)