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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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“Methods for selecting covariates for regression adjustment are well covered elsewhere (Greenland, 1989; Schisterman et al., 2009).”
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“Covariates were selected to adjust for important baseline influences while avoiding over-controlling for variables that might be intermediaries between delirium and AD (Schisterman et al., 2009).”
BackgroundRiley, E. D., Vittinghoff, E., Kagawa, R. M. C., Raven, M. C., Eagen, K., Cohee, A., Dilworth, S. E., & Shumway, M. (2020) · Journal of Urban Health · doi:10.1007/s11524-019-00404-x · Cited by 41
Revisiting Overadjustment Bias
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Self-citationAssociation of 5α-Reductase Inhibitors With Prostate Cancer Mortality
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“In the analyses of comorbidity and impairment described above, to avoid overadjustment bias 31 we did not adjust for age or sex or for comorbid psychopathology in the case of the impairment analysis.”
Core Concepts in Pharmacoepidemiology: New‐User Designs
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BackgroundSitting Time, Fidgeting, and All-Cause Mortality in the UK Women’s Cohort Study
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“In multivariate analysis, over-adjustment was assessed with the ratio of events-of-interest per the entered covariates, and a cutoff level of < 10 was interpreted as over-adjustment in this study [29,30].”
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Is Sedentary Behavior a Novel Risk Factor for Cardiovascular Disease?
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“Additionally, adjusting for several factors in a small population might produce misleading results [56, 57].”
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“Measuring covariates recorded only up to the start of an exposure window (possibly years before exposure) risks missing confounders and omitted variable bias, whereas including covariates recorded during or after the exposure window (hence, after the exposure) risks underestimation through unintended adjustment formed…”
BackgroundGed, Y. M. A., Sánchez, A., Patil, S., Knežević, A., Stein, E., Petruzella, S., Weiss, K. L., Düzgöl, C., Chaim, J. L., Akın, O., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-1389 · Cited by 38
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5α-Reductase Inhibitors and the Risk of Cancer-Related Mortality in Men With Prostate Cancer
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“This involves a risk of statistical overadjustment, which can obscure a true effect or create an apparent effect when none exists if adjusting for an intermediate variable on a causal path from exposure to outcome.(28) Patients’ experience of one health care aspect may affect the relationship between an experience of…”
BackgroundDissecting the effect of workplace exposures on workers’ rating of psychological health and safety
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“This is because upstream (distal) dimensions should be included in models to examine the effects of more proximal dimensions, while proximal dimensions should not be included in models for more distal dimensions.(25) For example, in recent studies of the COPSOQ, leadership resources have been positioned as distal fact…”
BackgroundIs the Oral Microbiome Associated with Blood Pressure in Older Women?
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Ultraprocessed beverages and processed meats increase the incidence of hypertension in Mexican women
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MethodThe impact of a fine-scale population stratification on rare variant association test results
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“Therefore, including it as a confounder could induce over-adjustment bias 35 if perceived stress is a mediator between dietary patterns and sleep quality, which is the rationale for adjusting for perceived stress only as a sensitivityanalysis.”
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“{Rothman, 2008}(58) {Schisterman, 2009}.(59)”
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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