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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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BackgroundAge‐dependent trajectories differ between within‐pair and extra‐pair paternity success
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“However, there is also potential benefit in including variables related only to the outcome (Zhang et al ., 2008; Moore and van der Laan, 2009), and there is risk in including variables related only to the treatment (Schisterman et al ., 2009).”
BackgroundInfluentialGondalia, R., Holliday, K. M., Baldassari, A., Justice, A. E., Stewart, J. D., Liao, D., Yanosky, J. D., Engel, S. M., Jordahl, K. M., Bhatti, P., et al. (2020) · Environmental Health Perspectives · doi:10.1289/ehp5360 · Cited by 26
“Moreover, erythrocyte and platelet counts—plausible intermediates of PM– leukocyte count associations—were neither uniformly available nor necessarily appropriate candidates for statistical adjustment (Schisterman et al. 2009).”
BackgroundShimmei, K., Nakamura, T. I., Ng, C. F. S., Hashizume, M., Murakami, Y., Maruyama, A., Misaki, T., Okabe, N., & Nishiwaki, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-63712-2 · Cited by 25
Geraets, A. F. J., Schram, M. T., Jansen, J. F. A., Koster, A., Dagnelie, P. C., van Greevenbroek, M. M. J., Stehouwer, C. D. A., Verhey, F. R. J., & Köhler, S. (2021) · Psychological Medicine · doi:10.1017/s0033291721000222 · Cited by 25
“This overadjustment might explain the loss of significant associations in models 4 and 5 in the analyses with WMH and periventricular WMH, and therefore we kept these factors in separate models (Schisterman et al., 2009).”
Antidepressant use and glycemic control
Mojtabai, R. (2013) · Psychopharmacology · doi:10.1007/s00213-013-2972-5 · Cited by 24
“Thus, BMI can be considered a mediator of any association between antidepressant use and increased risk of glycemic abnormalities and adjusting for BMI can distort the causal relationships between exposure and outcome (Schisterman et al. 2009).”
BackgroundMaslova, E., Hansen, S., Grunnet, L. G., Strøm, M., Bjerregaard, A. A., Hjort, L., Kampmann, F. B., Madsen, C. M., Baun Thuesen, A., Bech, B. H., et al. (2017) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.128637 · Cited by 26
“To avoid overadjustment and potential colliderstratification bias (42), suspected mediators (gestational weight gain, birth weight) were left out of the models; Spearman correlations with the exposure and outcomes were examined for potential insight into mediation pathways.”
MethodGeller, R. J., Decker, M. R., Adedimeji, A., Weber, K. M., Kassaye, S. G., Taylor, T., Cohen, J., Adimora, A. A., Haddad, L. B., Fischl, M. A., et al. (2020) · Journal of Women s Health · doi:10.1089/jwh.2019.7972 · Cited by 25
van Zwieten, A., Dai, J., Blyth, F., Wong, G., & Khalatbari‐Soltani, S. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad177 · Cited by 24
“7 Adjustment for a mediator closes a causal pathway from the exposure to the outcome, thereby reducing the total causal effect that is estimated.”
BackgroundMethodInfluentialSleep Duration Irregularity and Risk for Incident Cardiovascular Disease in the UK Biobank
Huang, T., Kianersi, S., Wang, H., Potts, K. S., Noordam, R., Sofer, T., Rutter, M. K., & Redline, S. S. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.040027 · Cited by 26
“To avoid potential overadjustment, 23 we considered these covariates in separate multivariable models.”
Association between pre-hospital vitamin D status and hospital-acquired new-onset delirium
Quraishi, S. A., Litonjua, A. A., Elias, K. M., Gibbons, F. K., Giovannucci, E. L., Camargo, C. A., & Christopher, K. B. (2015) · British Journal Of Nutrition · doi:10.1017/s0007114515001245 · Cited by 24
“Adjusted OR were estimated by multivariable logistic regression models, including a priori covariate terms considered to plausibly associate with both 25(OH)D levels and HANOD((8)) to avoid unnecessarily adjusting for variables that do not affect bias or the causal relationship between exposure and outcome((42)).”
MethodAndreucci, A., Campbell, P., Richardson, E., Chen, Y., & Dunn, K. M. (2019) · European Journal of Pain · doi:10.1002/ejp.1491 · Cited by 24
“the variable for which the analysis is adjusted for is in the pathway between the exposure and the outcome and adjustment may bias the results towards the null) (Schisterman et al., 2009) might explain the attenuation of estimates after adjustment for consultation frequency (i.”
ResultEstrada, P., Wang, H., & Farkas, T. (2019) · American Educational Research Journal · doi:10.3102/0002831219887137 · Cited by 24
Hagen, K., Stovner, L. J., & Zwart, J. (2020) · The Journal of Headache and Pain · doi:10.1186/s10194-020-01142-1 · Cited by 26
“However, to avoid over-adjustment bias [20], we adjusted only for the most established confounding factors in epidemiological studies.”
BackgroundHayek, S., Brinkman, T. M., Plana, J. C., Joshi, V. M., Leupker, R. V., Durand, J., Green, D. M., Partin, R. E., Santucci, A. K., Howell, R. M., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.2054 · Cited by 24
Natural Resource Dependence and Rural American Economic Prosperity From 2000 to 2015
Mueller, J. T. (2020) · Economic Development Quarterly · doi:10.1177/0891242420984512 · Cited by 26
“These variables were excluded to avoid overadjusting the model by conditioning on, or controlling for, downstream variables (Schisterman et al., 2009).”
BackgroundMethodInfluentialHao, S., Fang, Y., Pang, P., Yang, B., Jiang, X., & Yan, A. (2021) · Environmental Health and Preventive Medicine · doi:10.1186/s12199-021-00969-7 · Cited by 25
van der Heide, F. C. T., Steens, I. L. M., Geraets, A. F. J., Foreman, Y. D., Henry, R. M. A., Kroon, A. A., Kallen, C., van Sloten, T. T., Dagnelie, P. C., DONGEN, M. C. J. M. V., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.34753 · Cited by 24
Pham, C., Bekkering, S., O’Hely, M., Burgner, D. P., Thomson, S. M., Vuillermin, P., Collier, F., Marx, W., Mansell, T., Symeonides, C., et al. (2022) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2022.05.011 · Cited by 24
Miao, Q., Guo, Y., Erwin, E., Sharif, F., Berhe, M., Wen, S. W., & WALKER, M. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269158 · Cited by 27
“Covariates were adjusted in multivariable models for each perinatal outcome separately [41, 42].”
MethodThe Interplay Among Natural Menopause, Insomnia, and Cognitive Health: A Population-Based Study
Shieu, M. M., Braley, T. J., Becker, J. B., & Dunietz, G. L. (2023) · Nature and Science of Sleep · doi:10.2147/nss.s398019 · Cited by 25
“However, hormone replacement therapy is a potential mediator in the pathway between age at menopause and cognitive function, thus its inclusion in the models would result in over adjustment and bias effect estimates.(39) While some studies have shown potential influence of hormones such as estradiol and cortisol on th…”
BackgroundAlcohol consumption and microvascular dysfunction: a J-shaped association: The Maastricht Study
van der Heide, F. C. T., Eussen, S. J. P. M., Houben, A. J. H. M., Henry, R. M. A., Kroon, A. A., Kallen, C., Dagnelie, P. C., DONGEN, M. C. J. M. V., Berendschot, T. T. J. M., Schouten, J., et al. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01783-x · Cited by 26
“Main strengths of this study are the large size of this population-based cohort study with oversampling of individuals with type 2 diabetes, which enables accurate comparison of individuals with and without diabetes [73]; the large number of potential confounders that was considered [85]; and the use of state-of-the-a…”
BackgroundImplications of Advancing Paternal Age: Does It Affect Offspring School Performance?
Svensson, A. C., Abel, K. M., Dalman, C., & Magnusson, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024771 · Cited by 24
“However, these results are susceptible to colliderstratification bias, as the estimated indirect effect removed by adjusting for a mediator can itself be confounded if there are common causes between the mediator and the outcome [28].”
BackgroundBertilsson, M., Vaez, M., Wærn, M., Ahlborg, G., & Hensing, G. K. E. (2014) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-014-9518-5 · Cited by 24
“However, we considered sickness absence in 2008 to be in the pathway between determinants and outcome in 2009, and did not adjust for it to avoid over adjustment [44, 45].”
BackgroundLow-dose aspirin and survival from lung cancer: a population-based cohort study
McMenamin, Ú. C., Cardwell, C. R., Hughes, C. M., & Murray, L. M. (2015) · BMC Cancer · doi:10.1186/s12885-015-1910-9 · Cited by 24
“To prevent over-adjustment in the analysis of pre-diagnosis low-dose aspirin use, adjustments were only made for potential confounders recorded prior to cancer diagnosis [31, 32] (statin and beta-blocker use were also based upon prescriptions in the year prior to diagnosis).”
MethodKendzerska, T., Gershon, A. S., TOMLINSON, G. A., & Leung, R. S. T. (2015) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201505-294oc · Cited by 26
McCullough, L. E., Chen, J., Cho, Y. H., Khankari, N. K., Bradshaw, P. T., White, A. J., Teitelbaum, S. L., Terry, M. B., Neugut, A. I., Hibshoosh, H. H., et al. (2017) · Breast Cancer Research · doi:10.1186/s13058-017-0811-z · Cited by 24
“These covariates are on the causal pathway between prediagnostic RPA and mortality, and adjustment for a causal intermediate would result in biased parametric estimates [29, 30].”
BackgroundPaediatric obesity and cardiovascular risk factors – A life course approach
Araújo, J., & Ramos, E. (2017) · Porto Biomedical Journal · doi:10.1016/j.pbj.2017.02.004 · Cited by 27
Roustaei, Z., Vehviläinen-Julkunen, K. M., Tuomainen, T., Lamminpää, R., & Heinonen, S. T. (2018) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2018.05.025 · Cited by 24
The association of insomnia with future mental illness: is it just residual symptoms?
Biddle, D. J., Kelly, P. J., Hermens, D. F., & Glozier, N. (2018) · Sleep Health · doi:10.1016/j.sleh.2018.05.008 · Cited by 23
Chuang, Y., Cheng, A., Sun, H., Wang, J., Chen, Y., Sheng, W., & Chang, S. (2018) · Journal of Infection · doi:10.1016/j.jinf.2018.10.001 · Cited by 24
Beccia, A. L., Jesdale, W. M., & Lapane, K. L. (2020) · Annals of Epidemiology · doi:10.1016/j.annepidem.2020.03.012 · Cited by 29
“Covariates—We considered two additional types of variables: (1) adjustment and (2) descriptive; such grouping was used to avoid model overadjustment [57] while still providing sufficient description of the sample’s BE risk profile.”
MethodThe Extractive Industries Transparency Initiative (EITI) and trust in politicians
Villar, P. F. (2020) · Resources Policy · doi:10.1016/j.resourpol.2020.101713 · Cited by 26
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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