Open AccessMarginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations
Author(s) -
Paola Gilsanz,
Jessica Young,
M. Maria Glymour,
Eric J. Tchetgen Tchetgen,
Chloe W. Eng,
Karestan C. Koenen,
Laura D. Kubzansky
Publication year - 2021
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwab253
Subject(s) - confounding , marginal structural model , psychological intervention , life course approach , context (archaeology) , medicine , inverse probability , psychology , econometrics , gerontology , statistics , psychiatry , developmental psychology , mathematics , bayesian probability , biology , paleontology , posterior probability , pathology
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.