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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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“In addition, because we controlled for osteoporosis, which is in the hypothesized causal pathway between extensive opioid use and risk of fracture, it is also possible that the odds ratios for extensive opioid use were underestimated (44).”
BackgroundAssociation between community greenness and obesity in urban-dwelling Chinese adults
Huang, W., Yang, B., Yu, H.-Y., Bloom, M. S., Markevych, I., Heinrich, J., Knibbs, L. D., Leskinen, A., Dharmage, S. C., Jalaludin, B. B., et al. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135040 · Cited by 103
Hussein, M., Roux, A. V. D., & Field, R. I. (2016) · Journal of Urban Health · doi:10.1007/s11524-016-0085-2 · Cited by 102
Revealing the burden of obesity using weight histories
Stokes, A., & Preston, S. H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1515472113 · Cited by 99
“Finally, both approaches risk eliminating observations in which disease is a product of obesity itself, producing a classic instance of “overadjustment bias” (46).”
BackgroundA review of covariate selection for non‐experimental comparative effectiveness research
Sauer, B. C., Brookhart, M. A., Roy, J., & VanderWeele, T. J. (2013) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3506 · Cited by 98
One-year mortality among Danish intensive care patients with acute kidney injury: a cohort study
Gammelager, H., Christiansen, C., Johansen, M. B., Tønnesen, E., Jespersen, B., & Sørensen, H. (2012) · Critical Care · doi:10.1186/cc11420 · Cited by 93
“The physiological variables included in these scores may be part of the causal pathway and adjustment may thereby attenuate any true association [35].”
BackgroundAssociation of Neighborhood Deprivation With Epigenetic Aging Using 4 Clock Metrics
Lawrence, K. G., Kresovich, J. K., O’Brien, K. M., Hoang, T. T., Xu, Z., Taylor, J. A., & Sandler, D. P. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.24329 · Cited by 101
To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies
Etminan, M., Brophy, J. M., Collins, P. G. S., Nazemipour, M., & Mansournia, M. A. (2021) · American Heart Journal · doi:10.1016/j.ahj.2021.03.008 · Cited by 98
Association Between Food Insecurity and HIV Viral Suppression: A Systematic Review and Meta-Analysis
Aibibula, W., Cox, J., Hamelin, A.-M., McLinden, T., Klein, M. B., & Brassard, P. (2016) · AIDS and Behavior · doi:10.1007/s10461-016-1605-5 · Cited by 100
The Association Between Chronic Exposure to Traffic-Related Air Pollution and Ischemic Heart Disease
Beckerman, B., Jerrett, M., Finkelstein, M. M., Kanaroglou, P., Brook, J. R., Arain, M. A., Sears, M. R., Stieb, D. M., Balmes, J. R., & Chapman, K. R. (2012) · Journal of Toxicology and Environmental Health · doi:10.1080/15287394.2012.670899 · Cited by 101
“The DAG shows that including NO2 and the road buffer variable together in the same model would have induced overadjustment bias (Schisterman et al. 2009) in the association between NO2 and IHD, as NO2 is essentially on the causal path between the road buffer and IHD.”
BackgroundAmrock, S. M., Duell, P. B., Knickelbine, T., Martin, S. S., O'Brien, E. C., Watson, K. E., Mitri, J., Kindt, I., Shrader, P., Baum, S. J., et al. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.10.006 · Cited by 99
Wlazlo, N., van Greevenbroek, M. M. J., Ferreira, I., Feskens, E. J. M., Kallen, C., Schalkwijk, C. G., Bravenboer, B., & Stehouwer, C. D. A. (2014) · Diabetes Care · doi:10.2337/dc13-2804 · Cited by 98
“Adjustments for an ascending proxy are not always prudent, as they can attenuate estimates due to differences in bias and variance between waist circumference and C3 (44).”
BackgroundQiu, S., Cai, X., Yin, H., Zügel, M., Sun, Z., Steinacker, J. M., & Schümann, U. (2016) · Metabolism · doi:10.1016/j.metabol.2016.02.006 · Cited by 96
“However, this could reduce the risk of reporting bias resulting from the selection of adjusted coefficients and over-adjustment bias related to control for multiple confounders [58].”
BackgroundHuang, H.-B., Pan, W., Chang, J.-W., Chiang, H.-C., Guo, Y. L., Jaakkola, J. J. K., & Huang, P. (2016) · Environmental Research · doi:10.1016/j.envres.2016.11.014 · Cited by 95
“We have no reason to expect that an iodine or selenium deficiency would be associated with phthalate exposure or that any deficiencies in these substances would affect the precision of the estimated effects rather than the estimates themselves (Schisterman et al., 2009).”
BackgroundAssociation of Prenatal Exposure to Endocrine-Disrupting Chemicals With Liver Injury in Children
Midya, V., Colicino, E., Conti, D. V., Berhane, K. T., Garcia, E., Stratakis, N., Andrušaitytė, S., Basagaña, X., Casas, M., Fossati, S., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.20176 · Cited by 101
To preserve or not to preserve
Mersereau, J. E., Goodman, L. R., Deal, A. M., Gorman, J. R., Whitcomb, B. W., & Su, H.-C. I. (2013) · Cancer · doi:10.1002/cncr.28317 · Cited by 95
Association between serum folate and vitamin B-12 and outcomes of assisted reproductive technologies
Gaskins, A. J., Chiu, Y., Williams, P. L., Ford, J. B., Toth, T. L., Hauser, R. B., & Chavarro, J. E. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.112185 · Cited by 95
“In addition, by analyzing the data from all women who initiated treatment, and not just from only women who underwent an embryo transfer, we were able to avoid the potential of bias introduced by conditioning on an important intermediate endpoint (36).”
Reduced Breastfeeding Rates in Firstborn Late Preterm and Early Term Infants
Hackman, N. M., Alligood-Percoco, N. R., Martin, A., Zhu, J., & Kjerulff, K. H. (2016) · Breastfeeding Medicine · doi:10.1089/bfm.2015.0122 · Cited by 95
“We did not control for time to first feeding, breastfeeding initiation, or latching problems because these were factors that we considered to be intermediate variables on the causal path between exposure (preterm and early term birth) and the outcome of interest in this study (breastfeeding success).(7) All analyses w…”
MethodMeasuring gender when you don’t have a gender measure: constructing a gender index using survey data
Smith, P. M., & Koehoorn, M. W. (2016) · International Journal for Equity in Health · doi:10.1186/s12939-016-0370-4 · Cited by 100
Adjustments and their Consequences-Collapsibility Analysis using Graphical Models
Greenland, S., & Pearl, J. (2011) · International Statistical Review · doi:10.1111/j.1751-5823.2011.00158.x · Cited by 91
“We then consider situations in which adjustment may partially remove or introduce a potential source of bias in estimating causal effects, and some additional special cases useful for case-control studies, cohort studies with loss, and trials with noncompliance (nonadherence).”
BackgroundKnipe, D. W., Carroll, R., Thomas, K. H., Pease, A. S., Gunnell, D., & Metcalfe, C. (2015) · BMC Public Health · doi:10.1186/s12889-015-2301-5 · Cited by 100
“Each SEP measure may contribute to the causal pathway between other SEP measures and suicide/attempted suicide risk and adjustment can bias the results towards the null [56].”
ResultMendelian Randomization Study of Body Mass Index and Colorectal Cancer Risk
Thrift, A. P., Gong, J., Peters, U., Chang-Claude, J. C., Rudolph, A., Slattery, M. L., Chan, A. T., Locke, A. E., Kahali, B., Justice, A. E., et al. (2015) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-14-1309 · Cited by 97
“, adjusting for menopausal hormone therapy) (2).”
Wang, D., Dai, X., Mishra, S. R., Lim, C. C. W., Carrillo-Larco, R. M., Gakidou, E. E., & Xu, X. (2022) · The Lancet Public Health · doi:10.1016/s2468-2667(22)00157-8 · Cited by 100
Semen quality in relation to antioxidant intake in a healthy male population
Zareba, P., Colaci, D., Afeiche, M. C., Gaskins, A. J., Jørgensen, N., Mendiola, J., Swan, S. H., & Chavarro, J. E. (2013) · Fertility and Sterility · doi:10.1016/j.fertnstert.2013.08.032 · Cited by 94
“1) R210 48 34 (26, 44) 104 (77, 140) 55 (51, 59) 8.”
The Role of Ambient Ozone in Epidemiologic Studies of Heat-Related Mortality
Reid, C. E., Snowden, J. M., Kontgis, C. P., & Tager, I. B. (2012) · Environmental Health Perspectives · doi:10.1289/ehp.1205251 · Cited by 98
“Although the concepts of confounding and mediation have long been defined in health sciences, in practice the two structures have often been conflated [for discussion of one example, see Schisterman et al. (2009)].”
BackgroundMaslova, E., Strøm, M., Olsen, S. F., & Halldorsson, T. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057261 · Cited by 92
“This analysis was performed to examine the stability of the estimates but we are cautious about adding these seven covariates, some of which are conditioning on future conditions, since we might be imposing overadjustment and bias in our analyses [37,38].”
MethodKaufman, E. J., Morrison, C. N., Branas, C. C., & Wiebe, D. J. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.0190 · Cited by 91
Socioeconomic status and type 2 diabetes complications among young adult patients in Japan
舟越, 光., Azami, Y., Matsumoto, H., Ikota, A., Ito, K., Okimoto, H., Shimizu, N., Tsujimura, F., Fukuda, H., Miyagi, C., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176087 · Cited by 95
“Below, we discuss the results in the model without adjustment of HbA1C and duration of diabetes, because the relationships were typically null-biased estimates of the total causal effect after adjusting for the intermediate variables [31].”
Yang, T., Peterson, K. E., Meeker, J. D., Sánchez, B. N., Zhang, Z., Cantoral, A., Solano, M., & Téllez‐Rojo, M. M. (2017) · Environmental Research · doi:10.1016/j.envres.2017.03.038 · Cited by 92
“We elected to adopt a confounders-only approach to the a priori selection of covariates (Schisterman et al., 2009).”
MethodOn a Class of Bias-Amplifying Variables that Endanger Effect EstimatesPreprint
Pearl, J. (2010) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.1203.3503 · Cited by 188
“If one has a 8See (Pearl, 2009a, pp. 339–41) for further explanation of this bias mechanism, which seems to have escaped the taxonomies in (Hernán et al., 2004) and (Schisterman et al., 2009).”
Kosar, C. M., Tabloski, P. A., Travison, T. G., Jones, R. N., Schmitt, E. M., Puelle, M. R., Inloes, J. B., Saczynski, J. S., Marcantonio, E. R., Meagher, D. J., et al. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)00006-6 · Cited by 89
Beavers, K. M., Beavers, D. P., Newman, J. J., Anderson, A. M., Loeser, R. F., Nicklas, B. J., Lyles, M. F., Miller, G. D., Mihalko, S. L., & Messier, S. P. (2014) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2014.11.005 · Cited by 89
Weisskopf, M. G., Sparrow, D., Hu, H., & Power, M. C. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1408888 · Cited by 89
“…to obtain the study sample, either because of selection bias (known as collider stratification bias in causal theory) (Hernán et al. 2004) or because the selection process is equivalent to conditioning on an intermediate between the exposure of interest and the outcome (Schisterman et al. 2009).”
BackgroundInfluentialInappropriate prescribing for the elderly—a modern epidemic?
Nyborg, G. A., Straand, J., & Brekke, M. (2012) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-012-1223-8 · Cited by 88
“In order to avoid overadjustment bias in the estimate of OR (number of doctors), the number of drugs was treated as an effect mediator and was omitted as a variable in the final regression model [43].”
Methodvan Cooten, M. H., Bilal, S. M., Gebremedhin, S., & Spigt, M. (2018) · Maternal and Child Nutrition · doi:10.1111/mcn.12631 · Cited by 92
Everson, T. M., Vives-Usano, M., Seyve, E., Cárdenas, A., Lacasaña, M., Craig, J. M., Lesseur, C., Baker, E., Fernández‐Jiménez, N., Heude, B., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24558-y · Cited by 88
“We did not adjust for GA because we hypothesized that it was likely downstream of the epigenetic response to MSDP or possibly on the causal pathway between MSDP and changes in DNAm; in either case, it would likely result in overadjustment 24 .”
BackgroundLifestyle Factors in the Association of Shift Work and Depression and Anxiety
Xu, M., Yin, X., & Gong, Y. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.28798 · Cited by 101
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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