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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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“For this, multivariate metaregression analysis was recruited to control for potential effect modifiers or any association between group differences and various assays [77,78].”
MethodMendelian randomization for nephrologists
Dobrijevic, E. L. K., van Zwieten, A., Kiryluk, K., Grant, A. J., Wong, G., & Teixeira-Pinto, A. M. (2023) · Kidney International · doi:10.1016/j.kint.2023.09.016 · Cited by 50
Hansen, B. T., Campbell, S. H., Burger, E. A., & Nygård, M. (2015) · Preventive Medicine · doi:10.1016/j.ypmed.2015.04.024 · Cited by 51
Zhao, Q., Li, S., Cao, W., Liu, D.-L., Qian, Q., Ren, H., Ding, F., Williams, G. M., Huxley, R., Zhang, W., et al. (2018) · Environmental Health Perspectives · doi:10.1289/ehp3062 · Cited by 49
“Some studies argue that it would cause downward bias if past infections were on the causal path of the overall exposure–lag–response association (Barnett et al. 2017; Schisterman et al. 2009).”
BackgroundMols, F., Schoormans, D., Smit, J. W. A., Netea-Maier, R. T., Links, T. P., van der Graaf, W. T. A., & Husson, O. (2018) · Head & Neck · doi:10.1002/hed.25325 · Cited by 49
“Adding these variables to multivariable analyses would result in bias because of over adjustment.(16) Furthermore, general HRQOL and disease-specific symptoms (ie, mean scores on the EORTC-QLQ-C30 and THYCA-QOL) between thyroid cancer survivors of different age groups were compared by means of ANOVA.”
MethodMoran, R. A., García‐Rayado, G., de la Iglesia, D., Martínez-Moneo, E., Fort, E., Lauret-Braña, E., Concepción, M., Ausania, F., Prieto-Martínez, C., González-de-Cabo, M., et al. (2018) · United European Gastroenterology Journal · doi:10.1177/2050640618798155 · Cited by 50
Exploring Inflammatory Mediators in Fetal and Maternal Compartments During Human Parturition
Menon, R., & Taylor, B. D. (2019) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000003470 · Cited by 49
An Introduction to Causal Diagrams for Anesthesiology Research
Gaskell, A. L., & SLEIGH, J. W. (2020) · Anesthesiology · doi:10.1097/aln.0000000000003193 · Cited by 49
Everson, T. M., O’Shea, T. M., Burt, A., Hermetz, K. E., Carter, B. S., Helderman, J., Hofheimer, J. A., McGowan, E. C., Neal, C. R., Pastyrnak, S. L., et al. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00942-1 · Cited by 49
Georgakis, M. K., Dessypris, N., Papadakis, V., Tragiannidis, A., Bouka, E., Hatzipantelis, E., Moschovi, M. A., Papakonstantinou, Ε., Polychronopoulou, S., Sgouros, S., et al. (2019) · Cancer Epidemiology · doi:10.1016/j.canep.2019.01.017 · Cited by 48
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Positive Airway Pressure and Cognitive Disorders in Adults With Obstructive Sleep Apnea
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Adipocytokines, C-Reactive Protein, and Cardiovascular Disease: A Population-Based Prospective Study
Seven, E., Husemoen, L. L. N., Sehested, T. S. G., Ibsen, H., Wachtell, K., Linneberg, A. R., & Jeppesen, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128987 · Cited by 50
“Nonetheless, in the context of leptin and CVD, the possibility of over-adjustment bias needs to be briefly discussed [35].”
BackgroundCognitive State following Stroke: The Predominant Role of Preexisting White Matter Lesions
Kliper, E., Assayag, E. B., Tarrasch, R., Artzi, M., Korczyn, A., Shenhar-Tsarfaty, S., Aizenstein, O., Hallevi, H., Mike, A., Shopin, L., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105461 · Cited by 47
“Additionally, in order to avoid over-adjustment and increase of net bias, age was not entered directly to the SEM models [54].”
MethodPreadmission metformin use and mortality among intensive care patients with diabetes: a cohort study
Christiansen, C. F., Johansen, M. B., Christensen, S., O’Brien, J. M., Tønnesen, E., & Sørensen, H. T. (2013) · Critical Care · doi:10.1186/cc12886 · Cited by 48
“In an additional analysis we adjusted for organ dysfunction, although this variable may be in the causal pathway [38].”
Kim, J., Mersereau, J. E., Khankari, N. K., Bradshaw, P. T., McCullough, L. E., Cleveland, R. J., Shantakumar, S., Teitelbuam, S. L., Neugut, A. I., Senie, R. T., et al. (2016) · Cancer Causes & Control · doi:10.1007/s10552-016-0716-7 · Cited by 49
“Adjusting for mediators is likely to attenuate and reduce the precision of the effect estimate for the PCOS–breast cancer association [45].”
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Association of childhood abuse and prescription opioid use in early adulthood
Austin, A. E., Shanahan, M. E., & Zvara, B. J. (2017) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.08.033 · Cited by 48
Revisiting the Table 2 fallacy: A motivating example examining preeclampsia and preterm birth
Bandoli, G., Palmsten, K. K., Chambers, C., Jelliffe‐Pawlowski, L. L., Baer, R. J., & Thompson, C. A. (2018) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12474 · Cited by 52
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“However, if there are unmeasured common causes of the mediator and outcome (Figure 1, variable U) (16), conditioning on the mediator will introduce a collider stratification bias (17,18).”
BackgroundReducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach
Laine, J. E., Baltar, V. T., Stringhini, S., Gandini, M., Chadeau‐Hyam, M., Mika Kivimaki, B., Severi, G., Perduca, V., Hodge, A. M., Dugué, P., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz248 · Cited by 48
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Prenatal Opioid Analgesics and the Risk of Adverse Birth Outcomes
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A Prospective Study of Back Pain and Risk of Falls Among Older Community-dwelling Men
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“added to the model (32,33) in order to avoid unnecessary adjustments (34).”
BackgroundAkobirshoev, I., Vetter, M., Iezzoni, L. I., Rao, S. R., & Mitra, M. (2022) · Health Affairs · doi:10.1377/hlthaff.2022.00509 · Cited by 48
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“However, the same studies simultaneously adjusted for potential intermediate variables, including premature rupture of membranes (PROM), fever, and time from rupture until delivery [14,16], which precludes estimation of the total effect of histologic chorioamnionitis on PTB [18].”
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“However, PIH and GDM could also be intermediate variables on the causal pathway between an air pollution exposure and PTB and thus including them in the model may be an overadjustment (Schisterman et al., 2009).”
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Barreiro, V. B., Pértega-Díaz, S., García-Rodríguez, T., González-Martín, C., Pértega, R. P., González-Dopeso, L. Y., & Seoane‐Pillado, T. (2023) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.11.041 · Cited by 47
Gerber, Y., Koren‐Morag, N., Myers, V., Benyamini, Y., Goldbourt, U., Drory, Y., & for the Israel Study Group on First Acute Myocardial Infarction (2011) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1177/1741826710389371 · Cited by 47
Smoking Is a Risk Factor for Graft Failure and Mortality after Renal Transplantation
Agarwal, P. K., Hellemons, M. E., Zelle, D. M., van Ree, R. M., van den Born, J., van der Heide, J. J. H., Gans, R. O. B., van Son, W. J., Navis, G., & Bakker, S. J. L. (2011) · American Journal of Nephrology · doi:10.1159/000328903 · Cited by 47
“Statistical Analysis Data were analyzed with SPSS version 14.0 (SPSS Inc., Chicago, Ill., USA), and GraphPad Prism version 4.03 (GraphPad Software Inc., San Diego, Calif., USA). Normally distributed variables are given as means ± standard deviation, whereas variables with a skewed distribution are given as median (int…”
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Infertility treatment and children's longitudinal growth between birth and 3 years of age
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“We did not limit our sample by birth size because we wanted to evaluate the total effect of infertility treatment, which may be in part due to mediating effects on size for gestational age (Schisterman et al., 2009).”
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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