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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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“” One also finds reference to the term overadjustment in settings with unnecessary control for variables.(7) In summary, overadjustment sometimes means control (eg, by regression adjustment, stratification, restriction) for a variable that (a) increases rather than decreases net bias or (b) affects precision without a…”
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Childhood Adversity as a Predictor of Non-Adherence to Statin Therapy in Adulthood
Korhonen, M. J., Halonen, J. I., Brookhart, M. A., Kawachi, I. I., Pentti, J., Karlsson, H., Mika Kivimaki, B., & Vahtera, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127638 · Cited by 22
“variables may be over-adjusted [33], the model was first adjusted for age at statin initiation only (Model 1) and then further for education and marital status (Model 2).”
MethodGrubic, N., Johnston, A., Randhawa, V. K., Humphries, K. H., Rosella, L. C., & Maximova, K. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.12.022 · Cited by 24
Villar, J., Ochieng, R., Staines-Urias, E., Fernandes, M., Ratcliff, M. J., Purwar, M., Barros, F. C., Horta, B. L., Ismail, L. C., Albernaz, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61917-z · Cited by 20
Disability outcomes of early cerebellar and brainstem symptoms in multiple sclerosis
Le, M. V., Malpas, C. B., Sharmin, S., Horáková, D., Havrdová, E., Trojano, M., Izquierdo, G., Eichau, S., Özakbaş, S., Lugaresi, A., et al. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520926955 · Cited by 21
Hayashi, C., Tanaka, H., & Ogata, S. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-01882-4 · Cited by 20
“This could be considered as over-adjustment bias due to an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome [45].”
BackgroundMortier, P., Vilagut, G., Alayo, I., Ferrer, M., Amigo, F., Aragonès, E., Peña, A. A., del Barco, A. A., Campos, M. T., Espuga, M., et al. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.02.009 · Cited by 20
“Since causal relationships between the included proximal risk factors are largely unknown, we refrained from constructing a fully adjusted multivariable model to avoid the risk of overadjustment bias (Schisterman et al., 2009).”
Antidepressant Use and Mortality Among Patients With Hepatocellular Carcinoma
Huang, K.-L., Chen, Y., Stewart, R. J., & Chen, V. C. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.32579 · Cited by 20
Xia, B., Li, Y., Hu, L., Xie, P., Mi, N., Lv, L., Liang, Z., Sun, Y., Li, Y., Jiang, X., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03809-x · Cited by 20
“Thirdly, considering the association between dietary intake and BMI, which can influence health outcomes [60], we examined the potential over-adjustment bias by comparing associations estimated from models both with and without BMI categories.”
Bay, B., Mortensen, E. L., & Kesmodel, U. S. (2014) · Fertility and Sterility · doi:10.1016/j.fertnstert.2014.06.048 · Cited by 19
The Use of Epidemiology in Risk Assessment: Challenges and Opportunities
Christensen, K. L. Y., Christensen, C. H., Wright, J. M., Galizia, A., Glenn, B. S., Scott, C. S., Mall, J. K., Bateson, T. F., Murphy, P. A., & Cooper, G. S. (2014) · Human and Ecological Risk Assessment An International Journal · doi:10.1080/10807039.2014.967039 · Cited by 20
“For a more complete explanation of the use of causal diagrams in modeling decisions, see these citations: Howards et al. (2012), Schisterman et al. (2009), and VanderWeele (2009).”
BackgroundResurrecting the Third Variable: A Critique of Pearl's Causal Analysis of Simpson's Paradox
Armistead, T. W. (2014) · The American Statistician · doi:10.1080/00031305.2013.807750 · Cited by 20
“…40 controls 1 Pearl’s point that we should not condition on noncausal variables has been made by many others, (Breslow and Day 1980, Greenland and Neutra 1980; Weinberg 1993; Greenland et al. 1999; Rothman, Greenland, and Lash 2008, pp. 131–134, 194–196, and 260; Schisterman, Cole, and Platt 2009).”
BackgroundPersistence of Underweight Status Among Late Preterm Infants
Goyal, N. K. (2012) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2011.1496 · Cited by 20
“We then assessed correlation between covariates using bivariate analyses; if 2 variables were colinear, such as insurance type and income, we chose 1 for multivariable analysis to avoid overadjustment bias.(42) We built our base multivariable model and added interaction terms between gestational age and other covariat…”
Tanamas, S. K., Permatahati, V., Ng, W. L., Backholer, K., Wolfe, R. S., Shaw, J. E., & Peeters, A. (2016) · BMC Obesity · doi:10.1186/s40608-016-0085-5 · Cited by 20
“posity measures and metabolic variables in this study and were excluded from the final model to avoid over adjustment [17].”
Female Infertility and “Emerging” Organic Pollutants of Concern
Bloom, M. S., Micu, R., & Neamtiu, I. A. (2016) · Current Epidemiology Reports · doi:10.1007/s40471-016-0060-1 · Cited by 20
Deconfounding confounding part 2: using directed acyclic graphs (DAGs)
Attia, J., Oldmeadow, C. J., Holliday, E. G., & Jones, M. P. (2017) · The Medical Journal of Australia · doi:10.5694/mja16.01167 · Cited by 20
Age-related macular degeneration and mortality: the Melbourne Collaborative Cohort Study
McGuinness, M. B., Finger, R. P., Karahalios, A., Guymer, R. H., English, D. R., Chong, E. W. T., Hodge, A. M., Robman, L. D., Giles, G. G., & Simpson, J. A. (2017) · Eye · doi:10.1038/eye.2017.139 · Cited by 20
Sadananthan, S. A., Tint, M. T., Michael, N., Aris, I. M., Loy, S. L., Lee, K. J., Shek, L. P., Yap, F. K. P., Tan, K. H., Godfrey, K. M., et al. (2019) · Obesity · doi:10.1002/oby.22408 · Cited by 19
“Because feeding practices and dietary intake are potential causal intermediates of the link between ethnicity and fat accumulation, adding them as covariates in a simple multiple regression model can lead to bias in the estimate of the total effect of ethnicity because of statistical overadjustment (43).”
BackgroundWang, T., McCullough, L. E., White, A. J., Bradshaw, P. T., Xu, X., Cho, Y. H., Terry, M. B., Teitelbaum, S. L., Neugut, A. I., Santella, R. M., et al. (2019) · Cancer · doi:10.1002/cncr.32364 · Cited by 20
Bell, S., & Britton, A. R. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-1845 · Cited by 20
Statistics and Causality: Separated to Reunite-Commentary on Bryan Dowd's “Separated at Birth”
Pearl, J. (2011) · Health Services Research · doi:10.1111/j.1475-6773.2011.01243.x · Cited by 20
“Methods for devising critical statistical tests by which two competing theories can be distinguished ( Pearl and Paz 2010 ) .”
BackgroundTemporal trend of cadmium exposure in the United States population suggests gender specificities
Ferraro, P. M., Sturniolo, A., Naticchia, A., D'Alonzo, S., & Gambaro, G. (2011) · Internal Medicine Journal · doi:10.1111/j.1445-5994.2011.02627.x · Cited by 20
Gerber, Y., Gabriel, K. P., Jacobs, D. R., Liu, J. Y., Rana, J. S., Sternfeld, B., Carr, J. J., Thompson, P. D., & Sidney, S. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae272 · Cited by 19
Epidemiology of Hajj pilgrimage mortality: Analysis for potential intervention
Gaddoury, M. A., & Armenian, H. K. (2023) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2023.05.021 · Cited by 19
Kluzek, S., Rubin, K. H., Sanchez-Santos, M. T., O’Hanlon, M. S., Andersen, M., Glintborg, D., & Abrahamsen, B. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02604-w · Cited by 19
Chen, S., Cai, Q., Xu, Y., Fu, Q., Feng, Y., Chen, X., Dai, S., Zhao, D., Zhan, C., Xu, W., et al. (2021) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x211002677 · Cited by 19
Differences in Perinatal Outcomes of Birthing People in Same-Sex and Different-Sex Marriages
Downing, J. M., Everett, B. G., & Snowden, J. M. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab148 · Cited by 22
IS HAPPINESS U-SHAPED IN AGE EVERYWHERE? A METHODOLOGICAL RECONSIDERATION FOR EUROPE
Bartram, D. (2022) · National Institute Economic Review · doi:10.1017/nie.2022.1 · Cited by 19
“It bears emphasising that this is not (yet) the standard practice in the social sciences — though it is well established in epidemiology (e.g. Schisterman et al., 2009).”
Pham, C., Vryer, R., O’Hely, M., Mansell, T., Burgner, D. P., Collier, F., Symeonides, C., Tang, M. L. K., Vuillermin, P., Gray, L. E. K., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094601 · Cited by 19
“When two factors had a strong Pearson correlation, only one was used as a covariate to minimize overadjustment bias [117].”
MethodScime, N. V., Metcalfe, A., Nettel‐Aguirre, A., Tough, S., & Chaput, K. H. (2023) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.14516 · Cited by 18
Role of Antioxidants of Natural Herbs in Management of Male Infertility
Gaznee, Kohli, A., & Kumar, R. (2023) · Journal for Research in Applied Sciences and Biotechnology · doi:10.55544/jrasb.2.1.9 · Cited by 19
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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