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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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“These reduced correlations observed when adjusting for the EPIC center could be due to potential lower statistical power and possibly over-adjustment [42].”
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Alampi, J. D., Lanphear, B. P., MacFarlane, A. J., Oulhote, Y., Braun, J. M., Muckle, G., Arbuckle, T. E., Ashley‐Martin, J., Hu, J. M. Y., Chen, A., et al. (2024) · Environmental Health Perspectives · doi:10.1289/ehp14479 · Cited by 18
“47 48 – 50 We collected covariate information via a questionnaire administered on study enrollment.”
MethodRisk of intracranial hypertension with intrauterine levonorgestrel
Etminan, M., Luo, H., & Gustafson, P. (2015) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098615588084 · Cited by 16
“com 113 risk factors and not confounders may have changed the results of our study [Schisterman et al. 2009].”
ResultKwok, C. S., Yeong, J. K.-Y., Turner, R. M., Cavallazzi, R., Singh, S., & Loke, Y. K. (2011) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-011-1159-4 · Cited by 16
Rind, E., Jones, A., & SOUTHALL, H. R. (2013) · Social Science & Medicine · doi:10.1016/j.socscimed.2013.12.004 · Cited by 16
Wallert, J., Lissåker, C., Madison, G., Held, C., & Olsson, E. M. G. (2017) · European Journal of Preventive Cardiology · doi:10.1177/2047487317693951 · Cited by 16
“Following established epidemiological practice, adjusting for socioeconomic status variables is therefore probably incorrect.(37) With such adjustments, we would expect attenuation of the CA–adherence association.”
MethodRea, F., Bonassi, S., Vitale, C., Trifirò, G., Cascini, S., Roberto, G., Chinellato, A., Lucenteforte, E., Mugelli, A., Corrao, G., et al. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4206 · Cited by 16
Chae, W. R., Nagel, J. M., Kuehl, L. K., Gold, S. M., Wingenfeld, K., & Otte, C. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.044 · Cited by 16
Self-assessment of clinical competence with LGBT patients at a pediatric hospital
Boskey, E. R., Taghinia, A. H., & Ganor, O. (2019) · Social Work in Health Care · doi:10.1080/00981389.2019.1588189 · Cited by 16
Braun, J. M., Kalloo, G., Kingsley, S. L., & Li, N. (2019) · Environment International · doi:10.1016/j.envint.2019.05.071 · Cited by 17
Douwes, R. M., Neto, A. W. G., Eisenga, M. F., Loon, E. V., Schutten, J. C., Gans, R. O. B., Naesens, M., van den Berg, E., Sprangers, B., Berger, S. P., et al. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003140 · Cited by 16
“In one analysis we excluded diabetic nephropathy, vitamin K antagonists, platelet inhibitors, and use of anti-hypertensive drugs from the final model, and in another analysis we excluded diabetes and cardiovascular disease history from the final model [35,36].”
MethodClinical risk factors for complete and partial placental retention – a case-control study
Klar, M., Laub, M., Schulte-Moenting, J., Proempeler, H., & Kunze, M. (2013) · Journal of Perinatal Medicine · doi:10.1515/jpm-2012-0260 · Cited by 16
“As a result, the true effect of previous cesarean deliveries on placental retention in subsequent pregnancy cannot be consistently estimated and the observed measure of association is expected to create a bias towards the null [13, 14].”
BackgroundRisk factors for delayed antiretroviral therapy initiation among HIV-seropositive patients
Fatukasi, T., Cole, S. R., Moore, R. D., Mathews, W. C., Edwards, J. K., Eron, J. J., & for the CNICS investigators (2017) · PLoS ONE · doi:10.1371/journal.pone.0180843 · Cited by 16
“Multiple adjusted effects from a single model should be interpreted with caution, and the potential for overadjustment bias, as well as adjusting for unnecessary variables should not be overlooked as these adjustments can result in increased bias or reduced precision [18, 19].”
BackgroundSelf-citationKponee, K. Z., Weisskopf, M. G., Grashow, R. G., Rotem, R. S., Coull, B. A., Schnaas, L., Hernández-Chávez, M. D. C., Sánchez, B. N., Peterson, K. E., Hu, H., et al. (2020) · NeuroToxicology · doi:10.1016/j.neuro.2020.02.013 · Cited by 16
Welander-Vatn, A., Ystrøm, E., Tambs, K., Neale, M. C., Kendler, K. S., Reichborn‐Kjennerud, T., & Knudsen, G. P. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.09.038 · Cited by 17
“Over-adjustment may partly explain the results from the multivariate co-twin control model (Schisterman et al., 2009).”
ResultRisk of Endometrial Cancer in Women with Diabetes: A Population-Based Retrospective Cohort Study
Zabulienė, L., Kačėnienė, A., Steponavičienė, L., Linkevičiūtė-Ulinskienė, D., Stukas, R., Arlauskas, R., Vansevičiūtė-Petkevičienė, R., & Smailytė, G. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10163453 · Cited by 16
“Excessive adjustment can introduce biases, such as selection bias [26,27].”
BackgroundPostpartum Hospital Readmissions Among Massachusetts Women Who are Deaf or Hard of Hearing
McKee, K. S., Akobirshoev, I., McKee, M. M., Li, F., & Mitra, M. (2022) · Journal of Women s Health · doi:10.1089/jwh.2022.0068 · Cited by 15
“25 To avoid over-adjustment bias, 26 we used a step-wise approach.”
Schoettler, M. L., Bhatt, H., & Vasu, S. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1064203 · Cited by 16
“Thus, adjusting for these complications in a typical multivariable biomarker analysis may not be the best approach (59).”
BackgroundBozigar, M., Huang, T., Redline, S. S., Hart, J. E., Grady, S. T., Nguyen, D., James, P., Nicholas, B., Levy, J. I., Laden, F., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp10959 · Cited by 17
“42,81 The nSES metric comprised a summed z -score of SES-related census variables (e.g., race, education, income, home value, nativity, unemployment) at the level of the census tract.”
Rengasamy, M. L., Moriarity, D. P., Kraynak, T. E., Tervo‐Clemmens, B., & Price, R. B. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01621-4 · Cited by 17
“Such results are not surprising given that cross-sectional analyses are unable to fully account for relevant complex causal, bidirectional, and/or moderation effects between PIMs, depression, and multiple covariates [14, 29].”
BackgroundChildhood adverse life events and skeletal muscle mitochondrial function
Duchowny, K. A., Mau, T., Diaz-Ramierz, L. G., Lui, L.-Y., Marcinek, D. J., Toledo, F. G. S., Cawthon, P. M., Hepple, R. T., Kramer, P. A., Newman, A. B., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj6411 · Cited by 15
“We note that this final model may represent overcontrol given the adjustment for potential mediators, and results should therefore be interpreted with caution ( 45 ).”
BackgroundResultVu, L., Quyen, B. T. T., Shelley, D., NIAURA, R. S., Bao, T. Q., Pham, N. Q., Nguyen, L. T., Chu, A., Pratt, A., Pham, C. T. L., et al. (2024) · International Journal of Public Health · doi:10.3389/ijph.2024.1607104 · Cited by 15
“As the study examined different measurements of SES, unnecessary adjustment may occur as these variables can be highly correlated [20].”
BackgroundCosta, E. L., Costa, J. F., Santos, M. P., Ladeira, L. L. C., Silva, R. A., & Ribeiro, C. C. C. (2017) · Caries Research · doi:10.1159/000479435 · Cited by 15
“DAGs are causal diagrams defined a priori by researchers to permit a more adequate control of confounding factors and, at the same time, prevent overcontrol bias and unnecessary adjustments [Schisterman et al., 2009].”
BackgroundUrban Densification and 12‐Year Changes in Cardiovascular Risk Markers
Chandrabose, M., Owen, N. G., Giles‐Corti, B., Turrell, G., Carver, A., & Sugiyama, T. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.013199 · Cited by 15
“Because of potential overadjustment, we did not adjust for physical activity variables (eg, walking) that may mediate the relationships examined.(25)”
MethodKomulainen, K., Gluschkoff, K., Velázquez, R. G., Airaksinen, J., Szmulewicz, A., & Jokela, M. M. (2020) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5447 · Cited by 15
Mohajer, B., Kwee, R. M., Guermazi, A., Berenbaum, F., Wan, M., Zhen, G., Cao, X., Haugen, I. K., & Demehri, S. (2021) · The Journal of Rheumatology · doi:10.3899/jrheum.210189 · Cited by 15
Sacha, C. R., Souter, I. C., Williams, P. L., Chavarro, J. E., Ford, J. B., Mahalingaiah, S., Donahoe, P. K., Hauser, R. B., Pépin, D., & Mínguez‐Alarcón, L. (2021) · Environment International · doi:10.1016/j.envint.2021.106809 · Cited by 15
“In addition, we included wellknown predictors in the fully adjusted models regardless of statistical significance since it has been demonstrated that this can improve the precision of the exposure estimates in the model (Schisterman et al., 2009).”
MethodEnvironmental Exposure to Triclosan and Male Fecundity: A Prospective Study in China
Zhu, W., Xie, C., Zhao, S., Zhang, D., & Zhang, H. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.814927 · Cited by 15
Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease
Yu, D., Wang, L., Kong, D., & Hongtu Zhu, B. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2087658 · Cited by 18
“…with the outcome except through the exposure (known as instrumental variables) may result in loss of efficiency in the causal effect estimate (e.g., Schisterman, Cole, and Platt 2009), while inclusion of variables only related to the outcome but not the exposure (known as precision variables) may…”
BackgroundCarrier, F. M., Deshêtres, A., Guerra, S. F., Rioux‐Massé, B., Zaouter, C., Lee, N. J., Amzallag, É., Joosten, A., Massicotte, L., & Chassé, M. (2022) · Transplantation · doi:10.1097/tp.0000000000004333 · Cited by 15
“Mostly, we did not include other nonconfounding outcome determinants (of the association between preoperative fibrinogen level and bleeding outcomes, such as donor characteristics) or other intraoperative variables occurring after the exposure that could be either mediators or a common consequence of the exposure and…”
BackgroundCausal modelling, mechanism, and probability in epidemiology
Kincaid, H. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199574131.003.0004 · Cited by 15
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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