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Handling Time Varying Confounding in Observational Research Publisher Pubmed



Mansournia MA1 ; Etminan M2 ; Danaei G3, 4 ; Kaufman JS5 ; Collins G6
Authors
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Authors Affiliations
  1. 1. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran
  2. 2. Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, BC, Canada
  3. 3. Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, United States
  4. 4. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States
  5. 5. Department of Epidemiology Biostatistics, and Occupational Health, McGill University, Montreal, Canada
  6. 6. Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

Source: BMJ (Online) Published:2017


Abstract

Many exposures of epidemiological interest are time varying, and the values of potential confounders may change over time leading to time varying confounding. The aim of many longitudinal studies is to estimate the causal effect of a time varying exposure on an outcome that requires adjusting for time varying confounding. Time varying confounding affected by previous exposure often occurs in practice, but it is usually adjusted for by using conventional analytical methods such as time dependent Cox regression, random effects models, or generalised estimating equations, which are known to provide biased effect estimates in this setting. This article explains time varying confounding affected by previous exposure and outlines three causal methods proposed to appropriately adjust for this potential bias: inverse-probability-of-treatment weighting, the parametric G formula, and G estimation. © Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to.
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