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Activity Number: 156 - Statistical Interactions – Making an Impact in Health Science
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #304525
Title: Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design
Author(s): Ai Ni* and Jaya M Satagopan
Companies: The Ohio State University and Memorial Sloan Kettering Cancer Center
Keywords: Additive interaction; Inverse-probability weighting; Multiple imputation; Offset; Stratified two-phase case-control design

There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this study is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified case-control design. Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probably weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.

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

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