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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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“We did not adjust for other risk factors such as smoking or body mass index, as these may be on the pathway between SES and blood pressure (35).”
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Kumar, A., Riviere, P. J., Luterstein, E., Nalawade, V. V., Vitzthum, L. K., Sarkar, R. R., Bryant, A. K., Einck, J. P., Mundt, A. J., Murphy, J. D., et al. (2020) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-020-0207-5 · Cited by 13
“Tumor characteristics, including T/N/M staging variables, Gleason score, PSA at diagnosis, and risk group were not included as covariates in our primary analyses because they are on the causal pathway between exposure (prediagnosis use of statins) and outcome (PCSM) [19].”
Collin, L. J., Yan, M., Jiang, R., Gogineni, K., Subhedar, P. D., Ward, K. C., Switchenko, J. M., Lipscomb, J., Miller‐Kleinhenz, J. M., Torres, M. A., et al. (2021) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2020.7694 · Cited by 13
“30 However, to be consistent with prior studies of race disparities in breast cancer outcomes, we present additional results from analyses adjusting for disease stage, SES, derived breast cancer subtype, and insurance type.”
ResultStructural Approach to Bias in Meta‐analyses
Shrier, I. (2011) · Research Synthesis Methods · doi:10.1002/jrsm.52 · Cited by 13
Dominici, F., Bargagli-Stoffi, F. J., & Mealli, F. (2021) · Harvard Data Science Review · doi:10.1162/99608f92.8102afed · Cited by 19
“For example, controlling for posttreatment variables (such as mediators) will bias the estimation of the causal effects see for example Ding and Miratrix, 2015; Greenland et al., 1999; M. A. Hernán et al., 2004; D. B. Rubin et al., 2008; Schisterman et al., 2009; Shrier, 2008.”
BackgroundKyriakopoulos, C. P., Taleb, I., Sideris, K., Maneta, E., Hamouche, R., Tseliou, E., Krauspe, E., Selko, S., Carter, S. J., Jones, T. L., et al. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.035464 · Cited by 12
“24 Multicollinearity among variables was examined using the variance inflation factor.”
Sexually transmitted infections and risk of hypertensive disorders of pregnancy
Taylor, B. D., Hill, A. V., Perez-Patron, M. J., Haggerty, C. L., Schisterman, E. F., Naimi, A. I., Noah, A. I., & Comeaux, C. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-17989-0 · Cited by 12
Self-citationPitter, J. G., Moizs, M., Ezer, É. S., Lukács, G., Szigeti, A., Repa, I., Csanádi, M., Mölken, M. R., Islam, M. K., Kaló, Z., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276719 · Cited by 15
“A less frequently recognized source of methodology bias is the adjustment of multivariate survival analyses to various post-baseline parameters in the putative causal chain [35], e.”
BackgroundEthnic differences and inequities in paediatric healthcare utilisation in the UK: a scoping review
Zhang, C. X., QUIGLEY, M. A., Bankhead, C. R., Bentley, T., Otasowie, C., & Carson, C. (2022) · Archives of Disease in Childhood · doi:10.1136/archdischild-2022-324577 · Cited by 12
“45 ► Studies interested in quantifying inequities in healthcare utilisation should ensure that choice of outcomes or analytical methods account for variation in healthcare need and health outcomes between ethnic groups.”
Littman, A. J., Peterson, A. C., Korpak, A. M., Czerniecki, J. M., Turner, A. P., Norvell, D. C., Williams, R. M., & Lehavot, K. (2023) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2023.02.011 · Cited by 12
Génard-Walton, M., McGee, G., Williams, P. L., Souter, I. C., Ford, J. B., Chavarro, J. E., Calafat, A. M., Hauser, R. B., & Mínguez‐Alarcón, L. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165536 · Cited by 12
Risk factors for late (28+ weeks’ gestation) stillbirth in the United States, 2014–2015
Tanner, D., Murthy, S., Ferres, J. L., Ramirez, J., & Mitchell, E. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289405 · Cited by 14
Data‐driven lifestyle patterns and risk of dementia in older Australian women
Dingle, S. E., Bowe, S. J., Bujtor, M., Milte, C. M., Daly, R. M., Byles, J., Cavenagh, D., & Torres, S. J. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13467 · Cited by 11
Vyas, M. V., Fang, J., de Oliveira, C., Austin, P. C., Yu, A., & Kapral, M. K. (2023) · Stroke · doi:10.1161/strokeaha.123.043369 · Cited by 12
“31,32 Also, lower attributable costs have been associated with poorer access to care and outcomes in New Zealand, 27 and future studies should explore similar association in other settings.”
Chung, E. O., Scherer, E. M., LeMasters, K. H., Bates, L. M., Hagaman, A. K., Staley, B. S., Zalla, L. C., Sikander, S., & Maselko, J. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001669 · Cited by 14
“In addition to confounders, we also included child gender, trial arm, and assessor as auxiliary variables in all models to improve precision [55].”
MethodCombs, M. P., Luth, J. E., Falkowski, N. R., Wheeler, D., Walker, N., Erb‐Downward, J. R., Wakeam, E., Sjoding, M. W., Dunlap, D. G., Admon, A. J., et al. (2024) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202308-1326oc · Cited by 14
Geddes‐Barton, D., Ramakrishnan, R., Knight, M., & Goldacre, R. (2024) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2024-222060 · Cited by 12
“19”
Lipp, R., Beltempo, M., Lodha, A. K., Weisz, D. E., McKanna, J., Matthews, I., Ricci, M. F., Hicks, M., Benlamri, A., Mukerji, A., et al. (2024) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2024.114270 · Cited by 12
Wardle, M. T., Reavis, K. M., & Snowden, J. M. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae141 · Cited by 12
Akobirshoev, I., Jerome, S. J., Snowden, J. M., Slaughter‐Acey, J., Valentine, A. M., Horner‐Johnson, W., & Mitra, M. (2024) · Disability and health journal · doi:10.1016/j.dhjo.2024.101765 · Cited by 12
Childhood adversity and late‐life cognitive and brain health in a diverse cohort
Hayes‐Larson, E., Gradwohl, N. M., Fong, J., Kobayashi, L. C., Gilsanz, P., Whitmer, R. A., Glymour, M. M., Barnes, L. L., Koenen, K. C., DeCarli, C. S., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14388 · Cited by 12
“44–51 We estimated the effect of childhood adversity on MRI outcomes andamyloidPETSUVRwithlinearregressionmodelsasfollows:Model Model 3, additionally adjusting for childhood family financial status, ever having insufficient money for food in childhood, and childhood familycommunitystanding.”
Patient- and Community-Level Characteristics Associated With Respiratory Syncytial Virus Vaccination
Surie, D., Yuengling, K. A., Safdar, B., Ginde, A. A., Peltan, I. D., Brown, S. M., Gaglani, M., Ghamande, S. A., Gottlieb, R., Columbus, C., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.2841 · Cited by 12
Dyer, B. P. (2025) · European Heart Journal Open · doi:10.1093/ehjopen/oeaf070 · Cited by 12
Improved Treatment of Multidrug-Resistant Bacterial Infections: Utility of Clinical Studies
Bettiol, E., Rottier, W. C., del Toro, M. D., Harbarth, S., Bonten, M. J. M., Rodríguez‐Baño, J., & on behalf of the COMBACTE consortium (2014) · Future Microbiology · doi:10.2217/fmb.14.35 · Cited by 13
Bager, L., Agerbo, E., Skipper, N., Thøgersen, M. H., & Laursen, T. M. (2020) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13203 · Cited by 12
“Alternatively, the adjustment could introduce bias, leaving the estimate imprecise (55), and consequently should be interpreted with caution.”
BackgroundIs socioeconomic position associated with bronchiolitis seasonality? A cohort study
Lewis, K. M., Stavola, B. L. D., & Hardelid, P. (2020) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2019-213056 · Cited by 12
Sociodemographic Characteristics and Physical Activity in Patients with COPD: A 3-Month Cohort Study
Dragnich, A. G., Yee, N. K., Gylys-Colwell, I., Locke, E. R., Nguyen, H. Q., Moy, M. L., Magzamen, S. L., & Fan, V. S. (2021) · COPD Journal of Chronic Obstructive Pulmonary Disease · doi:10.1080/15412555.2021.1920902 · Cited by 13
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“We removed insurance status and the socioeconomic status score from all adjusted models to reduce overadjustment bias [30] because of their role as hypothesized intermediate variables in the causal pathways between race and ethnicity (via racism) and PEP or PrEP use; insurance status and…”
Predictors of Food and Physical Activity Tracking Among Young Adults
O’Loughlin, E. K., Marashi, M., Sabiston, C. M., Lucibello, K. M., Sylvestre, M., & O’Loughlin, J. L. (2023) · Health Education & Behavior · doi:10.1177/10901981231159679 · Cited by 13
“We did not estimate a model including all predictors because adjustment by variables that are not confounders of the association between the exposure and the outcome (but rather colliders or mediators) could bias the estimated coefficient (Schisterman et al., 2009; Westreich & Greenland, 2013).”
BackgroundNeighborhood-level Socioeconomic Position During Early Pregnancy and Risk of Gastroschisis
Neo, D. T., Desrosiers, T. A., Martin, C. L., Carmichael, S. L., Güçsavaş‐Çalıkoğlu, M., Conway, K. M. C., Evans, S. P., Feldkamp, M. L., Gilboa, S. M., Insaf, T. Z., et al. (2023) · Epidemiology · doi:10.1097/ede.0000000000001621 · Cited by 12
Causal Diagrams and Three Pairs of Biases
Shahar, E., & Doron, J. (2012) · InTech eBooks · doi:10.5772/33486 · Cited by 12
“Causal-pathway bias due to conditioning on an intermediary is often called ‘overadjustment’, a poorly standardized term (Schisterman et al., 2009).”
BackgroundHwang, W. J., & Park, Y. (2015) · Journal of Korean Academy of Nursing · doi:10.4040/jkan.2015.45.6.857 · Cited by 12
Nansel, T. R., Lipsky, L. M., Eisenberg, M. H., Liu, A., Mehta, S. N., & Laffel, L. M. (2016) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2016.07.005 · Cited by 14
Sluik, D., Jankovic, N., Hughes, M., O’Doherty, M. G., Schöttker, B., Drygas, W., Rolandsson, O., Männistö, S., Ordóñez-Mena, J. M., Ferrières, J., et al. (2017) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2017.4 · Cited by 12
Statistical models and confounding adjustment
Cook, J. A., & Ranstam, J. (2017) · British journal of surgery · doi:10.1002/bjs.10245 · Cited by 12
Soft drink consumption and periodontal status in pregnant women
Menezes, C. C., Ribeiro, C. C. C., Alves, C. M. C., Thomaz, É. B. A. F., Franco, M. M., Batista, R. F. L., & da Sílva, A. A. M. (2018) · Journal of Periodontology · doi:10.1002/jper.16-0388 · Cited by 12
Horne, C. E., Goda, T. S., Nifong, L. W., Kypson, A. P., O’Neal, W. T., Kindell, L. C., Jindal, C., & Efird, J. T. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16010073 · Cited by 12
“The simultaneous adjustment for two or more interrelated variables within a factor may result in misleading effect estimates [42,43].”
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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