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Activity Number:
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176
- New England Statistical Society Invited Papers on Novel Developments and Future Directions of Statistics in Data Science
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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New England Statistical Society
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Abstract #320663
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Title:
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A Composite Endpoint for Treatment Benefit According to Patient Preference
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Author(s):
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Ying Lu* and Ruben van Eijk and Lu Tian and Lori Nelson
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Companies:
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Stanford University School of Medicine and University Medical Center Utrecht (UMCU), Utrecht, the Netherlands and Stanford University and Stanford University
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Keywords:
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Gap time analysis;
Survival analysisl;
Breast cancer;
Quantile regression;
Stochastic modeling
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Abstract:
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Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering individual patient preferences. In this talk, I presented a survey study of ALS patients to show the heterogeneity of functional symptom domains of ALSFRS-R from patient perspectives. I will then introduce a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. A new composite endpoint named Patient-ranked Order of Function (PROOF) has been proposed for evaluation of efficacy of amyotrophic lateral sclerosis (ALS) clinical trials. We use both theoretical and empirical methods to examine the statistical properties of our method and to compare with conventional approaches. We conclude that the proposed composite DOOR properly reflects patient-level preferences and can be used in pivotal trials or comparative effectiveness trials for a patient-centered evaluation of overall treatment benefits.
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Authors who are presenting talks have a * after their name.
