Predictive Margins with Survey Data
1999/06/01 by Barry I. Graubard, Edward L. Korn · 883 citations
Health Professions · Mathematics · Medicine · #Advanced Causal Inference Techniques #Analysis of covariance #Computer science #Covariance #Covariate #Econometrics #Environmental health #Food Security and Health in Diverse Populations #Generalization #Machine learning #Margin (machine learning) #Mathematics #Medicine #National Health Interview Survey #National Health and Nutrition Examination Survey #Statistical Methods and Bayesian Inference #Statistics #Survey data collection #Survey sampling
paper · doi:10.1111/j.0006-341x.1999.00652.x
published in Biometrics 55(2), 652-659 (Oxford University Press)
openalex publication_date 1999/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25
Abstract
In the analysis of covariance, the display of adjusted treatment means allows one to compare mean (treatment) group outcomes controlling for different covariate distributions in the groups. Predictive margins are a generalization of adjusted treatment means to nonlinear models. The predictive margin for group r represents the average predicted response if everyone in the sample had been in group r. This paper discusses the use of predictive margins with complex survey data, where an important consideration is the choice of covariate distribution used to standardize the predictive margin. It is suggested that the textbook formula for the standard error of an adjusted treatment mean from the analysis of covariance may be inappropriate for applications involving survey data. Applications are given using data from the 1992 National Health Interview Survey (NHIS) and the Epidemiologic Followup Study to the first National Health and Nutrition Examination Survey (NHANES I).
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