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Overadjustment bias and unnecessary adjustment in epidemiologic studies.
Open accessAbstract
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.
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“To ensure that we did not improperly adjust for mediating variables, 22 we used covariates measured at wave 1 instead of wave 2.”
From Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. (2019) · cites this paper as [22]
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Metadata
- Authors
- Schisterman, E. F., Cole, S. R., & Platt, R. W.
- Affiliations
- National Institutes of Health (US)
- University of North Carolina at Chapel Hill (US)
- McGill University (CA)
- Corresponding author
- Schisterman, Enrique F.
- Venue
- Epidemiology
- Published
- 2009-07
- Volume
- 20
- Issue
- 4
- Pages
- 488-495
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC2744485
- OpenAlex
- W2004722883
- Cited by
- 2,096
- References
- 39
- Citations by year
- 2026: 298 · 2025: 160 · 2024: 123 · 2023: 153 · 2022: 172 · 2021: 156 · 2020: 167 · 2019: 154 · 2018: 117 · 2017: 130 · 2016: 129 · 2015: 98 · 2014: 78 · 2013: 60 · 2012: 38
- FWCI
- 27.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (R03AI071763)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (Z01HD008761)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- intermediate variables, confounding, selection bias, causal graphs, maternal smoking, neonatal mortality, regression adjustment, stratification, sample size
- MeSH terms
- Female, Humans, Infant Mortality, Infant, Newborn, Population Dynamics, Smoking, Models, Statistical, Epidemiologic Research Design, Bias, Causality
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