Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations.
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...
| Published in: | American Journal of Epidemiology Vol. 191; no. 2; pp. 349 - 360 |
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| Main Authors: | , , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
Feb2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155241083&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155241083 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: Feb2022 vid: 191 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 155241083 155241083 155241083 10.1093/aje/kwab253 155241083 ppf: 349 ppct: 11 formats: tig: atl: Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations. aug: au: Gilsanz, Paola Young, Jessica G Glymour, M Maria Tchetgen, Eric J Tchetgen Eng, Chloe W Koenen, Karestan C Kubzansky, Laura D sug: subj: Models, Structural Simulations Human Socioeconomic Factors Depression Stroke Risk Factors ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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