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Activity Number:
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223
- Clinical Trials: Recent Statistical Advances for Enabling Personalized Medicine
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #322848
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View Presentation
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Title:
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Measuring Differential Treatment Benefit Across Marker Specific Subgroups: The Choice of Outcome Scale
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Author(s):
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Alexia Iasonos* and Jaya Satagopan
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Companies:
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Memorial Sloan Kettering Sloan Cancer Center and Memorial Sloan Kettering Cancer Center
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Keywords:
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predictive biomarker ;
interaction ;
survival ;
treatment benefit
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Abstract:
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Clinical and epidemiological studies of anticancer therapies increasingly seek to identify predictive biomarkers to obtain insights into variation in treatment benefit. For time to event endpoints, a predictive biomarker is typically assessed using the interaction between the biomarker and treatment in a proportional hazards model. Interactions are contrasts of outcomes and depend upon the choice of the outcome scale. In this paper we investigate interaction contrasts under three scales - the natural logarithm of hazard ratio, the natural logarithm of survival probability, and survival probability at a pre-specified time. We will present theoretical results and simulations that showed that an interaction effect may be required in a model simply because of the choice of scale of the outcome. These illustrate that we can have a non-zero interaction on survival or logarithm of survival probability scales even when there is no interaction on the logarithm of hazard ratio scale. We will provide an empirical illustration of the proposed methods for evaluating a predictive biomarker using data from a published melanoma study.
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Authors who are presenting talks have a * after their name.
