Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations

• Published in: American journal of epidemiology Vol. 191; no. 2; pp. 349 - 359
• Main Authors: Gilsanz, Paola, Young, Jessica G, Glymour, M Maria, Tchetgen Tchetgen, Eric J, Eng, Chloe W, Koenen, Karestan C, Kubzansky, Laura D
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 24.01.2022; Oxford Publishing Limited (England)
• Subjects: Bias; Causality; Computer Simulation; Data Interpretation, Statistical; Depression - epidemiology; Depression - etiology; Epidemiology; Health risks; Humans; Mathematical models; Mimicry; Missing data; Models, Structural; Models, Theoretical; Practice of Epidemiology; Regression analysis; Regression models; Risk analysis; Risk Factors; Social conditions; Statistical analysis; Stroke - psychology; Structural models; social epidemiology; confounding; simulation; bias; marginal structural models; inverse probability weighting; life course
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwab253

Abstract Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability–weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.