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Activity Number: 375 - Modern Statistical Methods for Comparative Effectiveness Research
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #300036
Title: Model-Assisted Sensitivity Analysis for Hidden Bias in CER
Author(s): Bo Lu* and Giovanni Nattino
Companies: The Ohio State University and The Ohio State University
Keywords: hidden bias; sensitivity analysis; matching design; conditional logistic model; trauma care

CER studies are vulnerable to the hidden bias. The impact of unmeasured covariates on the intervention effect can be assessed by conducting a sensitivity analysis. A comprehensive framework of sensitivity analyses has been developed for matching designs. Sensitivity parameters are introduced to capture the association between the missing confounder and the exposure or the outcome. Fixing sensitivity parameter values, it is possible to compute the bounds of the p-value of a randomization test on causal effects. We propose a model assisted sensitivity analysis with binary outcomes for the general 1:k matching design, which provides results equivalent to the conventional nonparametric approach in large sample. Our method substantially simplifies the implementation and interpretation of the sensitivity analysis. More importantly, we are able to provide a closed form representation for the set of sensitivity parameters for which the maximum p-values are non-significant. This methodology can be easily extended to matching designs with multilevel treatments. We illustrate our method using a U.S. trauma care database to examine mortality difference between trauma care levels.

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

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