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Activity Number: 184
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #313243
Title: Increase Chance for Recovery vs. Risk Reduction
Author(s): Lev S. Sverdlov*+
Companies: Redmond Analytics
Keywords: Chance for recovery ; Spontaneous recovery ; Sensitivity to treatment ; Treatment-induced recovery ; Treatment response prediction ; Risk reduction
Abstract:

The indices of risk reduction are commonly used to assess the effect of treatment. The concept of increase chance for recovery is a logical equivalent to risk reduction, but using it for analysis can be problematic because spontaneous and treatment-induced recoveries are not phenotypically distinguishable. Changing a conceptual framework and switching focus on recovery might bring an ability of identifying patients capable of spontaneous recovery, improve prediction of treatment response, facilitate search for biomarkers, and reduce risk for adverse effects via eliminating unnecessary prescriptions. We will use a hidden variable approach to assess chance for treatment-induced and spontaneous recovery, and will focus on creating the interface between the content area and statistical approach, the development of a conceptual, structural and logical model, facilitating statistical model selection and the interpretation of the results. Clinical, experimental and epidemiological data will be presented to justify the hidden variables. Logical analysis of the dependencies between the hidden and observed variables will be performed to narrow the range of priors for Bayesian analysis.


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