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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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A non-technical explanation of the counterfactual definition of confounding.
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Morrow, E. L., Duff, M. C., & Mayberry, L. S. (2022) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-21-00551 · Cited by 33
Blood eosinophil count and risk of pneumonia hospitalisations in individuals with COPD
Vedel‐Krogh, S., Nordestgaard, B. G., Lange, P., Vestbo, J., & Nielsen, S. F. (2018) · European Respiratory Journal · doi:10.1183/13993003.00120-2018 · Cited by 32
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Prenatal Exposure to Nonpersistent Environmental Chemicals and Postpartum Depression
Jacobson, M. H., Hamra, G. B., Monk, C. E., Crum, R. M., Upadhyaya, S. G., Avalos, L. A., Bastain, T. M., Barrett, E. S., Bush, N. R., Dunlop, A. L., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.3542 · Cited by 32
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“Season of wear is not a confounder (not associated with the outcome) but explains considerable variance in the exposure and is included to improve the precision of estimates (35).”
BackgroundKoh, G. C. K. W., Vlaar, A. P. J., Hofstra, J. J., de Jong, H. K., van Nierop, F. S., Peacock, S. J., Wiersinga, W. J., Schultz, M. J., & Juffermans, N. P. (2012) · Critical Care Medicine · doi:10.1097/ccm.0b013e31824e1696 · Cited by 31
“We sought to avoid over adjustment bias and unnecessary adjustment (28) by identifying confounders using a conceptual hierarchical framework/directed acyclic graph (see details in Supplementary Fig.”
MethodChioléro, A., Paradis, G., & Kaufman, J. S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt228 · Cited by 31
Adverse Childhood Experiences and Alcohol Consumption in Midlife and Early Old-Age
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“…betweenACEs and alcohol consumption, for example, experiencing ACEs could affect the socio-economic group or marital status a participant belongs to in adulthood, which in turn might affect drinking—so therefore controlling for these factors may represent over-adjustment (Schisterman et al., 2009).”
BackgroundAssociation of Alcohol With Mortality After Traumatic Brain Injury
Albrecht, J. S., Afshar, M., Stein, D. M., & Smith, G. S. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx254 · Cited by 31
“Because alcohol consumption may both predispose individuals to injury, and influence the severity of injury, traditional adjustment for this factor likely introduced an over-adjustment bias in multivariable logistic regression (35).”
BackgroundMethodBelaroussi, Y., Bouteiller, F., Bellera, C. A., Pasquier, D., Pérol, M., Debieuvre, D., Filleron, T., Girard, N., Schött, R., Mathoulin‐Pélissier, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-36623-1 · Cited by 30
A multistate framework for the analysis of subsequent injury in sport (M‐FASIS)
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“However, these same variables usually need to be excluded from the regression equation if they are an effect of the exposure of interest to avoid introducing overadjustment bias (Schisterman et al., 2009).”
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“A potential reason is that previous studies have not focused primarily on educational differences and their models have included covariates which are possible mediators of the effect of education, increasing the risk of overadjustment (Schisterman et al., 2009).”
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Association of Prenatal Depression With New Cardiovascular Disease Within 24 Months Postpartum
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“However, the use of salvage therapy would affect the interpretation and not the validity of our effect estimates because salvage therapy would function as an intermediate (which should not be adjusted) [46] and not a confounder in our analysis.”
BackgroundBell, S., Britton, A. R., Kubínová, R., Malyutina, S. K., Pająk, A., IuP, N., & Bobák, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104384 · Cited by 32
“Separate models were fitted for each drinking measure as including all drinking components in the same model would likely represent over-adjustment [47] and therefore bias the effect estimates (estimates from models including all drinking variables simultaneously are made available for interested readers in Informatio…”
MethodIkeda, T., Tsuboya, T., Aida, J., Matsuyama, Y., Koyama, S., Sugiyama, K., Kondo, K., & Osaka, K. (2019) · Family Practice · doi:10.1093/fampra/cmz022 · Cited by 30
Early-life neighborhood context, perceived stress, and preterm birth in African American Women
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Association of Social Mobility With the Income-Related Longevity Gap in the United States
Venkataramani, A. S., Daza, S., & Emanuel, E. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.6532 · Cited by 31
Liew, Z., & Ernst, A. (2021) · Current Environmental Health Reports · doi:10.1007/s40572-020-00301-5 · Cited by 32
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Ponsonby, A. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa174 · Cited by 25
“In logistic regression, every additional covariate removes at least one degree of freedom.(27) To limit the number of covariates required to be adjusted subsequently, one can consider preprocessing the measure to standardize for relevant process factors (Box 1, Step 3.”
BackgroundThe Role of Nutritional Factors in Asthma: Challenges and Opportunities for Epidemiological Research
Bédard, A., Li, Z., Ait‐hadad, W., Camargo, C. A., Leynaert, B., Pison, C. M., Dumas, O., & Varraso, R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063013 · Cited by 35
“Thus, there is an additional issue with time-dependent confounding that needs to be accounted for and, in this situation, standard methods of analysis may provide biased results [114,115].”
Background
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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