Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment

Genetic Epidemiology
Jaya M SatagopanQin Zhou

Abstract

The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatmen...Continue Reading

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Citations

May 10, 2016·Journal of Dermatological Science·P RubegniG Cevenini
Oct 5, 2018·Current Allergy and Asthma Reports·Antonella Muraro, Stefania Arasi

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