Covariates as Random Effects (CaRE): An approach to modeling high-dimensional confounding and effect heterogeneity

Area of Science:

• Epidemiology
• Biostatistics
• Statistical Modeling

Background:

• Conventional regression models assume additive covariate effects, potentially leading to misspecification and misleading estimates, especially with complex confounding.
• High-dimensional data and intricate confounding structures challenge traditional single-level regression models.
• Interactions between covariates are often overlooked in standard analyses, obscuring subgroup-specific associations.

Purpose of the Study:

• To introduce and evaluate the Covariates as Random Effects (CaRE) approach for regression analysis.
• To provide a flexible alternative to strictly additive models for handling complex covariate structures and interactions.
• To demonstrate the utility of multilevel regression for covariate adjustment and effect heterogeneity assessment in epidemiologic research.

Main Methods:

• The Covariates as Random Effects (CaRE) approach treats combinations of covariates as strata, utilizing random intercepts for flexible modeling of interactions.
• Incorporation of random slopes allows for the exploration of how exposure-outcome associations vary across different strata.
• The approach was illustrated using data from older adults in India, examining the education-cognitive function association.

Main Results:

• The CaRE approach provides a more parsimonious representation of complex covariate structures compared to conventional models.
• Multilevel specifications using CaRE yielded similar average exposure-outcome associations while highlighting variability across strata.
• The method effectively captures interactions between covariates through stratification and partial pooling.

Conclusions:

• The Covariates as Random Effects (CaRE) approach offers a robust alternative for covariate adjustment in regression analysis.
• This multilevel modeling strategy enhances the assessment of effect heterogeneity in epidemiologic studies.
• CaRE facilitates a more nuanced understanding of exposure-outcome relationships in the presence of complex confounding.