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Activity Number: 497
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #320983 View Presentation
Title: Assessing Sensitivity to Unmeasured Confounding in Multilevel Models Using a Simulated Potential Confounder
Author(s): Nicole Carnegie* and Jennifer L. Hill and Vincent Dorie
Companies: University of Wisconsin - Milwaukee and New York University and New York University
Keywords: Hierarchical model ; Random effects model ; Mixed effects model ; Selection on observables ; Ignorability

A major obstacle to developing evidenced-based policy is the difficulty in implementing randomized experiments to answer all causal questions of interest. When using a non-experimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder when the outcome and/or treatment assignment exhibit multilevel structure. We characterize the individual-level confounder through two parameters that describe the relationships between 1) the confounder and the treatment assignment and 2) the confounder and the outcome variable. Our approach can be applied to both continuous and binary treatment variables

We demonstrate the efficacy of the method and its sensitivity to violations of the random effects assumption (group-level errors in treatment and outcome are not correlated) through simulations. We illustrate its potential usefulness in practice in the context of a non-randomized classroom-based nutrition intervention study.

Authors who are presenting talks have a * after their name.

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