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The Bias of Standard Methods in Estimating Causal Effect



Shakiba M1 ; Mansournia MA2 ; Soori H3
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Authors Affiliations
  1. 1. Neuroscience research center, Faculty of Health, Guilan University of Medical Sciences, Rasht, Iran
  2. 2. Department of Epidemiology, Faculty of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Source: Iranian Journal of Epidemiology Published:2017

Abstract

Standard methods for estimating exposure effects in longitudinal studies will result in biased estimates of the exposure effect in the presence of time-dependent confounders affected by past exposure. In the present review article, we first described the assumptions required for estimating the causal effect in longitudinal studies and their structure regarding various types of exposure and confounders; then, we explained the bias of standard methods in estimating the causal effect. Two types of bias, i.e. over-adjustment bias and selection bias, occur in estimating the effect of time-varying exposure in the presence of time-dependent confounders affected by previous exposure using standard regression analysis. Standard regression methods cannot sufficiently modify time-dependent confounders and estimate the total causal effect of the exposure. © 2017, Iranian Epidemiological Association. All Rights Reserved.
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