Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies

By Enrique F. Schisterman, Stephen R. Cole, Robert William Platt. 2009

1,886 citations recorded by the source corpus.

Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, neutral): “However, these methods for the static setting cannot be extended directly to time-varying treatments (Hernán et al., 2000; Schisterman et al., 2009).”
A new criterion for confounder selection (2011, background): “It is well known that control for such variables tends to increase the standard error of the estimates of treatment effects (e.g., Robinson and Jewell, 1991; Schisterman, Cole, and Platt, 2009).”
Temperature (2019, neutral): “Still another way to introduce the influence of ignored mechanical details is to modify the entropy s ( x ).”
On model selection and model misspeciﬁcation in causal inference (2010, neutral): “i=1 Ai π̂(Li) {Yi − ω̂1(Li)}φ1(Li) (11)”
On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice (2020, neutral): “See Schisterman et al. (2009) regarding this point.”