Using directed acyclic graphs to determine whether multiple imputation or subsample-multiple imputation estimates of an exposure-outcome association are unbiased.
Missing data are a pervasive problem in epidemiology, with multiple imputation (MI) a commonly used analysis method. MI is valid when data are missing at random (MAR). However, definitions of MAR with multiple incomplete variables are not easily interpretable and descriptions of graphical model-base...
| Publicado en: | American Journal of Epidemiology Vol. 195; no. 2; pp. 505 - 515 |
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| Autores principales: | , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191385332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191385332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: Feb2026 vid: 195 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 191385332 191385332 191385332 10.1093/aje/kwaf265 191385332 ppf: 505 ppct: 10 formats: tig: atl: Using directed acyclic graphs to determine whether multiple imputation or subsample-multiple imputation estimates of an exposure-outcome association are unbiased. aug: au: Madley-Dowd, Paul Hughes, Rachael A Mathur, Maya B Heron, Jon Tilling, Kate affil: MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United KingdomPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, United KingdomNIHR Biomedical Research Centre, University of Bristol, Bristol, United KingdomCentre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom sug: subj: Medical Records Data Analysis Selection Bias Models, Statistical Algorithms Epidemiological Research Causality Models, Theoretical ab: Missing data are a pervasive problem in epidemiology, with multiple imputation (MI) a commonly used analysis method. MI is valid when data are missing at random (MAR). However, definitions of MAR with multiple incomplete variables are not easily interpretable and descriptions of graphical model-based conditions are not accessible to applied researchers. Previous literature shows that MI may be valid in subsamples, even if not in the full dataset. Practical guidance on applying MI with multiple incomplete variables is lacking. We present an algorithm using directed acyclic graphs to determine when MI will estimate an exposure-outcome coefficient without bias. We extend the algorithm to assess whether MI in a subsample of the data, in which some variables are complete, and the remaining are imputed, will be valid and unbiased for the exposure-outcome coefficient. We apply the algorithm to several simple exemplars, and in a more complex real-life example highlight that only subsample-MI of the outcome would be valid. Our algorithm provides researchers with the tools to decide whether to use MI in practice when there are multiple incomplete variables. Further work could focus on the likely size and direction of biases and the impact of different missing data patterns. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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