Daniel J Lizotte1, Mayuri Mahendran2, Siobhan M Churchill3, Greta R Bauer4. 1. Department of Computer Science, Faculty of Science and Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, Canada. Electronic address: dlizotte@uwo.ca. 2. Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, Canada. Electronic address: mmahend@uwo.ca. 3. Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, Canada. Electronic address: schurch9@uwo.ca. 4. Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, Canada. Electronic address: gbauer@uwo.ca.
Abstract
RATIONALE: Intersectionality has been increasingly adopted as a theoretical framework within quantitative research, raising questions about the congruence between theory and statistical methodology. Which methods best map onto intersectionality theory, with regard to their assumptions and the results they produce? Which methods are best positioned to provide information on health inequalities and direction for their remediation? One method, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), has been argued to provide statistical efficiency for high-dimensional intersectional analysis along with valid intersection-specific predictions and tests of interactions. However, the method has not been thoroughly tested in scenarios where ground truth is known. METHOD: We perform a simulation analysis using plausible data generating scenarios where intersectional effects are present. We apply variants of MAIHDA and ordinary least squares regression to each, and we observe how the effects are reflected in the estimates that the methods produce. RESULTS: The first-order fixed effects estimated by MAIHDA can be interpreted neither as effects on mean outcome when interacting variables are set to zero (as in a correctly-specified linear regression model), nor as effects on mean outcome averaged over the individuals in the population (as in a misspecified linear regression model), but rather as effects on mean outcome averaged over an artificial population where all intersections are of equal size. Furthermore, the values of the random effects do not reflect advantage or disadvantage of different intersectional groups. CONCLUSIONS: Because first-order fixed effects estimates are the reference point for interpreting random effects as intersectional effects in MAIHDA analyses, the random effects alone do not provide meaningful estimates of intersectional advantage or disadvantage. Rather, the fixed and random parts of the model must be combined for their estimates to be meaningful. We therefore advise caution when interpreting the results of MAIHDA in quantitative intersectional analyses.
RATIONALE: Intersectionality has been increasingly adopted as a theoretical framework within quantitative research, raising questions about the congruence between theory and statistical methodology. Which methods best map onto intersectionality theory, with regard to their assumptions and the results they produce? Which methods are best positioned to provide information on health inequalities and direction for their remediation? One method, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), has been argued to provide statistical efficiency for high-dimensional intersectional analysis along with valid intersection-specific predictions and tests of interactions. However, the method has not been thoroughly tested in scenarios where ground truth is known. METHOD: We perform a simulation analysis using plausible data generating scenarios where intersectional effects are present. We apply variants of MAIHDA and ordinary least squares regression to each, and we observe how the effects are reflected in the estimates that the methods produce. RESULTS: The first-order fixed effects estimated by MAIHDA can be interpreted neither as effects on mean outcome when interacting variables are set to zero (as in a correctly-specified linear regression model), nor as effects on mean outcome averaged over the individuals in the population (as in a misspecified linear regression model), but rather as effects on mean outcome averaged over an artificial population where all intersections are of equal size. Furthermore, the values of the random effects do not reflect advantage or disadvantage of different intersectional groups. CONCLUSIONS: Because first-order fixed effects estimates are the reference point for interpreting random effects as intersectional effects in MAIHDA analyses, the random effects alone do not provide meaningful estimates of intersectional advantage or disadvantage. Rather, the fixed and random parts of the model must be combined for their estimates to be meaningful. We therefore advise caution when interpreting the results of MAIHDA in quantitative intersectional analyses.
Authors: Greta R Bauer; Siobhan M Churchill; Mayuri Mahendran; Chantel Walwyn; Daniel Lizotte; Alma Angelica Villa-Rueda Journal: SSM Popul Health Date: 2021-04-16