ArticleHuman heredity2019
Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.
Article in Human heredity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- The Interaction between Education and Sex with Alcohol Consumption during the COVID-19 Pandemic: A Cross-Sectional Analysis of Two Brazilian Cities.International journal of environmental research and public health · 2024Article
- Maternal periconceptional alcohol consumption and gastroschisis in the National Birth Defects Prevention Study, 1997-2011.Paediatric and perinatal epidemiology · 2022Article
- Multiple health risk behaviors, including high consumption of ultra-processed foods and their implications for mental health during the COVID-19 pandemic.Frontiers in nutrition · 2022Article
- A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator.American journal of epidemiology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
BACKGROUND AND
aimsThere 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 two-phase 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 paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.
methodsUsing simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.
resultsSimulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.
conclusionWhen estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.
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