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Activity Number: 234
Type: Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #312650 View Presentation
Title: A Predictive Modeling Approach to Identify Clinical Important Treatment Difference in a Subgroup: Fraternal Twins Method
Author(s): Max Kuhn*+ and Birol Emir and Ed Whalen
Companies: Pfizer and Pfizer and Pfizer
Keywords: Data Mining ; Predictive Modeling ; Subgroup
Abstract:

As Good (1983) stated post-hoc analysis of the data is "dangerous, useful, and often done". However, within the ultimate framework of hypothesis/idea generation, it may become a "very fruitful" exercise. In this project we assumed (without loss of any generalizability) that the dataset contain p-(continuous) predictors, and a continuous outcome variable from two treatment groups (n from the drug first and m from the placebo). The algorithm is as follows: similar to Hodges-Lehmann (1973) we calculate all pairwise differences between the drug and placebo on the outcome variable and the predictors. For a clinically important cutoff C, we consider only the outcome differences > C, and use those in a predictive method such as CART. Cross Validation is used both for issues dealing with over fitting and dependency. We present an application on a clinical data and simulations are conducted to compare this method with Virtual Twins (2011), SIDES (2011) and ARF (2004).


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