Activity Number:
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69
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #320746
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Title:
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Characterizing Causal Treatment Effect Heterogeneity with Conditional Inference Trees
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Author(s):
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Julian Wolfson* and Lauren Erickson
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Companies:
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University of Minnesota and HealthPartners Institute for Education and Research
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Keywords:
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treatment effect heterogeneity ;
decision tree ;
conditional inference tree ;
matching
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Abstract:
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The main statistical task underlying personalized medicine is determining which population subgroups most benefit from a given treatment. We propose a technique for characterizing treatment effect heterogeneity which combines one-to-one matching with conditional inference trees. "Pseudo-subjects" are created by matching pairs of individuals with opposite treatment assignments but similar covariate values. The (causal) treatment effect and covariates for each pseudo-subject are respectively given by the within-pair difference in the outcome, and a set of "consensus" covariates for the pair. The pseudo-subject data are then used as inputs to a conditional inference tree; splits in the tree define distinct groups of pseudo-subjects with similar causal treatment effects. Splitting is governed by the multiply-adjusted p-values for marginal associations between consensus covariates and causal treatment effects, which are computed using linear mixed effects models to account for the correlation between pseudo-subjects induced by the matching. We illustrate the technique by applying it to data from a randomized trial evaluating the effects of portion size on weight gain and caloric intake.
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Authors who are presenting talks have a * after their name.
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