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Activity Number:
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48
- Academics Industry Perspectives on Cancer Data Innovations: Simultaneous Inference, Inconsistency, and Clinical Response
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Type:
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Topic-Contributed
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Date/Time:
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Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Biometrics Section
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Abstract #317328
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Title:
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Cure Models with Adaptive Activation for Modeling Cancer Survival
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Author(s):
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Sanjib Basu*
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Companies:
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University of Illinois Chicago
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Keywords:
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Bayesian survival analysis;
Breast cancer;
Colon cancer;
Identifiability;
Mixture cure model,
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Abstract:
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Cure rate models postulate a fraction of subjects to be eventually failure free and can be formulated via different approaches. The issue of identifiability has been a topic of substantial research in these models. We propose a general and flexible class of cure rate models motivated by analysis of colon cancer and triple-negative breast cancer survival data. We establish that the class is stochastically ordered in the activation parameter and also establish two identifiability results for this class. This class include various other cure rate models proposed in the literature as special cases. We illustrate that while some existing cure rate models may perform poorly under model misspecifications, the proposed model with adaptive activation provides appropriate inference in these cases. We apply the proposed approach to assess treatment-gender interaction on cure rate in a colon cancer study and to assess role of tumor heterogeneity and ethnic disparity in breast cancer
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Authors who are presenting talks have a * after their name.
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