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Research Methods & Reporting

Handling time varying confounding in observational research

BMJ 2017; 359 doi: https://doi.org/10.1136/bmj.j4587 (Published 16 October 2017) Cite this as: BMJ 2017;359:j4587
  1. Mohammad Ali Mansournia, assistant professor of epidemiology1,
  2. Mahyar Etminan, assistant professor of ophthalmology and visual sciences2,
  3. Goodarz Danaei, associate professor of global health3,
  4. Jay S Kaufman, professor of epidemiology4,
  5. Gary Collins, professor of medical statistics5
  1. 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran
  2. 2Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada
  3. 3Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
  4. 4Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
  5. 5Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
  1. Correspondence to: M A Mansournia mansournia_m{at}sina.tums.ac.ir]
  • Accepted 28 August 2017

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.

Summary points

  • Many exposures of epidemiological interest are time varying, and time varying confounding affected by past exposure often occurs in practice

  • Time varying confounding affected by past exposure is often adjusted for by using conventional analytical methods such as time dependent Cox regression, random effects models, or generalised estimating equations in clinical research, which are known to provide biased effect estimates in this setting

  • Three causal methods have been proposed to appropriately adjust for time varying confounders that are affected by past exposure: inverse-probability-of-treatment weighting, parametric G formula, and G estimation

Imagine you are a clinician scientist who is interested in examining the effect of testosterone treatment on risk of acute myocardial infarction. Recent studies have alluded to a possible harmful effect, although results have been contradictory.1 One reason might be inadequate adjustment for the time varying confounding that may occur by the change in serum testosterone levels over time, which in turn could affect treatment patterns in the future. After talking to a colleague, you are convinced that adjusting for …

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