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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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Cites
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,045
A structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004) · Epidemiology · doi:10.1097/01.ede.0000135174.63482.43 · Cited by 2,844
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355
Porta, M. (2026) · doi:10.1093/acref/9780197663639.001.0001 · Cited by 2,175
Martuza, V. R., & Kenny, D. A. (1981) · Educational Researcher · doi:10.2307/1175080 · Cited by 2,034
Identifiability and exchangeability for direct and indirect effects
Robins, J. M., & Greenland, S. (1992) · Epidemiology · doi:10.1097/00001648-199203000-00013 · Cited by 1,793
Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
Shaw, G. M., Wasserman, C. R., O’Malley, C. D., Nelson, V. E., & Jackson, R. J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00011 · Cited by 1,783
Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445 · cited 3×
“A set of rules for causal diagrams are succinctly described by Greenland et al10 and in the appendix of Hernan et al.11 Briefly, causal diagrams link variables by singleheaded (ie, directed) arrows that represent direct causal effects.”
BackgroundInfluentialCausation and Causal Inference in Epidemiology
Rothman, K. J., & Greenland, S. (2005) · American Journal of Public Health · doi:10.2105/ajph.2004.059204 · Cited by 1,260 · cited 1×
“” 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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Sleep and Sickness Absence: A Nationally Representative Register-Based Follow-Up Study
Lallukka, T. M., Kaikkonen, R., Härkänen, T., Kronholm, E., Partonen, T., Rahkonen, O., & Koskinen, S. V. P. (2014) · SLEEP · doi:10.5665/sleep.3986 · Cited by 86
Mínguez‐Alarcón, L., Sergeyev, O., Burns, J. S., Williams, P. L., Lee, M., Korrick, S., Smigulina, L., Revich, B., & Hauser, R. B. (2016) · Environmental Health Perspectives · doi:10.1289/ehp25 · Cited by 88
“In addition, we decided to include abstinence time regardless of statistical significance since this is a well-known predictor of most semen quality parameters, and thus can improve the precision of the exposure estimates in the model (Schisterman et al. 2009).”
Mendez, W. M., Eftim, S., Cohen, J. D., Warren, I., Cowden, J., Lee, J. S., & Sams, R. L. (2016) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/jes.2016.58 · Cited by 88
Bavinck, J. N. B., Feltkamp, M. C. W., Green, A. C., Fiocco, M. F., Euvrard, S., Harwood, C. A., Nasir, S., Thomson, J., Proby, C. M., Naldi, L., et al. (2017) · American Journal of Transplantation · doi:10.1111/ajt.14537 · Cited by 89
Curtis, J. R., Xie, F., Yang, S., Bernatsky, S., Chen, L., Yun, H., & Winthrop, K. L. (2018) · Arthritis Care & Research · doi:10.1002/acr.23769 · Cited by 87
Role of Thigh Muscle Changes in Knee Osteoarthritis Outcomes: Osteoarthritis Initiative Data.
Mohajer, B., Dolatshahi, M., Moradi, K., Najafzadeh, N., Eng, J., Zikria, B., Wan, M., Cao, X., Roemer, F., Guermazi, A., et al. (2022) · Radiology · doi:10.1148/radiol.212771 · Cited by 91
Sugar-sweetened beverage consumption and age at menarche in a prospective study of US girls
Carwile, J. L., Willett, W. C., Spiegelman, D. L., Hertzmark, E., Rich-Edwards, J. W., Frazier, A. L., & Michels, K. B. (2015) · Human Reproduction · doi:10.1093/humrep/deu349 · Cited by 86
“Increases in BMI may mediate the association between SSB consumption and earlier menarche; therefore, we did not include BMI as a covariate in primary models (Schisterman et al., 2009).”
BackgroundThe association between food insecurity and academic achievement in Canadian school-aged children
Faught, E. L., Williams, P. L., Willows, N. D., Asbridge, M., & Veugelers, P. J. (2017) · Public Health Nutrition · doi:10.1017/s1368980017001562 · Cited by 86
“As cross-sectional study designs, as used in the present study, are not considered to be appropriate for mediation analysis((47)), we attempted to evaluate the association between food insecurity and academic achievement outside its association via diet by adjusting for dietary indicators, at risk of overadjustment bi…”
MethodBarrett, P., McCarthy, F. P., Evans, M., Kublickas, M., Perry, I. J., Stenvinkel, P., Khashan, A. S., & Kublickiene, K. R. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003255 · Cited by 88
“Previous studies have controlled for postpartum hypertension when investigating associations between preeclampsia and cardiometabolic disease [16, 54], and despite an inherent risk of over-adjustment bias [55], the associations appear to persist.”
BackgroundGibb, S., Fergusson, D. M., & Horwood, L. J. (2012) · Social Science & Medicine · doi:10.1016/j.socscimed.2012.02.028 · Cited by 85
Autism and Convictions for Violent Crimes: Population-Based Cohort Study in Sweden
Heeramun, R., Magnusson, C., Gumpert, C. H., Granath, S., Lundberg, M., Dalman, C., & Rai, D. (2017) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2017.03.011 · Cited by 93
“Since other relevant, but later onset psychiatric disorders such as psychotic disorders, personality disorders or drug and alcohol misuse may be considered mediators or intermediate variables in the relationship, we did not adjust for them as this may introduce bias in the estimates 24 .”
BackgroundHafeman, D. M., Rooks, B., Merranko, J., Liao, F., Gill, M. K., Goldstein, T. R., Diler, R., Ryan, N., Goldstein, B. I., Axelson, D. A., et al. (2020) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.06.013 · Cited by 83
Franklin, J. M., Eddings, W., Glynn, R. J., & Schneeweiß, S. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv108 · Cited by 83
Lifetime physical activity and female stress urinary incontinence
Nygaard, I., Shaw, J. M., Bardsley, T. R., & Egger, M. J. (2015) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2015.01.044 · Cited by 88
“Thus, BMI is on the direct pathway between lifetime activity and SUI and is an effect of lifetime physical activity; including BMI could eliminate the association of activity with SUI by overadjustment.(29) We frequency matched controls to cases 1:1 by age (39-49, 50-60, 61-65 years), and selected controls using a com…”
MethodLiu, R., Aiello, A. E., Mensah, F. K., Gasser, C. E., Rueb, K., Cordell, B., Juonala, M., Wake, M. A., & Burgner, D. P. (2017) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2016-208646 · Cited by 88
“Adjustment for such variables in this situation is typically considered overadjustment and will increase bias towards the null hypothesis,[63] but residual confounding through grandparental factors, for example, may still bias our result in an unpredictable manner.”
BackgroundBjelland, E. K., Hofvind, S. S. H., Byberg, L., & Eskild, A. (2018) · Human Reproduction · doi:10.1093/humrep/dey078 · Cited by 84
“These factors may mediate the association of age at menarche with age at menopause, and adjustment for mediating factors may introduce biases in the estimates (Schisterman et al., 2009).”
BackgroundAutomated data-adaptive analytics for electronic healthcare data to study causal treatment effects
Schneeweiß, S. (2018) · Clinical Epidemiology · doi:10.2147/clep.s166545 · Cited by 84
“An overadjustment stemming from the adjustment of causal intermediates, reverse causation, or immortal person-time are general study design concerns that are addressable with principled study designs to support causal inference and, if present, can cause bias often larger in magnitude than confounding.(87,88) A common…”
BackgroundPotentially inappropriate medications in older adults: a population-based cohort study
Roux, B., Sirois, C., Simard, M., Gagnon, M.-È., & Laroche, M. (2019) · Family Practice · doi:10.1093/fampra/cmz060 · Cited by 85
Endometriosis and Risk of Adverse Pregnancy Outcome: A Systematic Review and Meta-Analysis
Breintoft, K., Pinnerup, R., Henriksen, T. B., Rytter, D., Uldbjerg, N., Forman, A., & Arendt, L. H. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040667 · Cited by 88
“Adjusting for ART may result in bias towards the null, thus leading to an underestimation of the association between endometriosis and adverse pregnancy outcomes [83].”
BackgroundPutrik, P., de Vries, N. K., Mujakovic, S., van Amelsvoort, L. G. P. M., Kant, I., Kunst, A. E., OERS, H. A. M. V., & Jansen, M. W. J. (2014) · Journal of Community Health · doi:10.1007/s10900-014-9894-y · Cited by 84
“[48].”
Is there a role for IGF ‐1 in the development of second primary cancers?
Shanmugalingam, T., Bosco, C., Ridley, A. J., & Hemelrijck, M. V. (2016) · Cancer Medicine · doi:10.1002/cam4.871 · Cited by 84
“Furthermore, treatment received for the primary cancer may confound the association between IGF1 and SPCs [114, 130].”
BackgroundO'Connor, A. M. C., Sargeant, J. M., Dohoo, I. R., Erb, H. N., Cevallos, M., Egger, M., Ersbøll, A. K., Martin, S. W., Nielsen, L. R., Pearl, D. L., et al. (2016) · Journal of Veterinary Internal Medicine · doi:10.1111/jvim.14592 · Cited by 93
“If variables are controlled unnecessarily in a regression model, the power is reduced, and the association between the exposure of interest and the outcome might be biased.(43,45,46)”
BackgroundStallings‐Smith, S., Mease, A., Johnson, T. M., & Arikawa, A. Y. (2018) · Environmental Research · doi:10.1016/j.envres.2018.06.041 · Cited by 81
“To prevent both over-adjustment bias and unnecessary adjustment, a causal diagram was generated based upon a thorough review of the literature to aid in identifying a minimum set of covariates that have the potential to affect both the exposure and the outcome (Greenland et al., 1999; Schisterman et al., 2009).”
Joyal‐Desmarais, K., Scharmer, A., Madzelan, M. K., See, J. V., Rothman, A. J., & Snyder, M. (2022) · Psychological Bulletin · doi:10.1037/bul0000377 · Cited by 99
Schmidt, S. A. J., Johansen, M. B., Olsen, M., Xu, X. P., Parker, J. M., Molfino, N. A., Lash, T. L., Sørensen, H. T., & Christiansen, C. F. (2014) · BMJ Open · doi:10.1136/bmjopen-2014-006720 · Cited by 83
“However, some of the clinical factors may be on the causal pathway linking AECOPD frequency to high mortality,(1) 2 8 9 making adjustment inappropriate.(27) Nevertheless, such information would have been useful in classifying AECOPD.”
BackgroundChiu, Y., Gaskins, A. J., Williams, P. L., Mendiola, J., Jørgensen, N., Levine, H., Hauser, R. B., Swan, S. H., & Chavarro, J. E. (2016) · Journal of Nutrition · doi:10.3945/jn.115.226563 · Cited by 81
Persistent organic pollutants and pregnancy complications
Smarr, M. M., Grantz, K. L., Zhang, C., Sundaram, R., Maisog, J. M., Barr, D. B., & Louis, G. M. B. (2016) · Science of The Total Environment · doi:10.1016/j.scitotenv.2016.02.030 · Cited by 80
“Furthermore, we found that gravid condition and serum POPs did not vary by variables reflective of socioeconomic status (i.e., education, income) and were excluded from logistic regression models in an attempt to avoid overadjustment bias (Schisterman et al., 2009).”
BackgroundMethodMigraine, obesity and body fat distribution – a population-based study
Kristoffersen, E. S., Børte, S., Hagen, K., Zwart, J., & Winsvold, B. S. (2020) · The Journal of Headache and Pain · doi:10.1186/s10194-020-01163-w · Cited by 83
Altenstadt, J. F. V. S. A., Hukkelhoven, C. W. P. M., van Roosmalen, J., & Bloemenkamp, K. W. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081959 · Cited by 78
“[31,32] We also assessed presence of effect modification between multiple pregnancy and PE using the likelihood-ratio test.”
MethodSpauwen, P. J. J., van Eupen, M. G. A., Köhler, S., Stehouwer, C. D. A., Verhey, F. R. J., Kallen, C., Sep, S. J. S., Koster, A., Schaper, N. C., Dagnelie, P. C., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-2754 · Cited by 80
“Therefore, we may have overadjusted our associations, resulting in a reduction in the potential total causal effect of AGEs on cognition by controlling for an intermediate variable (46).”
BackgroundHegvik, T.-A., Instanes, J. T., Haavik, J., Klungsøyr, K., & Engeland, A. (2018) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-017-1056-1 · Cited by 79
“Substantial attenuation of the estimated associations between ADHD and autoimmune diseases when adjusting for smoking and BMI, would indicate that much of the effect of ADHD on these diseases might be mediated through these mediators [57, 58].”
BackgroundBeccia, A. L., Baek, J., Jesdale, W. M., Austin, S. B., Forrester, S., Curtin, C., & Lapane, K. L. (2019) · Eating Behaviors · doi:10.1016/j.eatbeh.2019.05.002 · Cited by 77
On the Relative Nature of Overadjustment and Unnecessary Adjustment
VanderWeele, T. J. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a82f12 · Cited by 80
Flood-Exposure is Associated with Higher Prevalence of Child Undernutrition in Rural Eastern India
Rodriguez-Llanes, J. M., Ranjan-Dash, S., Mukhopadhyay, A., & Guha‐Sapir, D. (2016) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13020210 · Cited by 82
A Prospective Study of Back Pain and Risk of Falls Among Older Community-dwelling Women
Marshall, L. M., Litwack‐Harrison, S., Cawthon, P. M., Kado, D. M., Deyo, R. A., Makris, U. E., Carlson, H. L., & Nevitt, M. C. (2016) · The Journals of Gerontology Series A · doi:10.1093/gerona/glv225 · Cited by 77
Prudencio, M., Erben, Y., Marquez, C. P., Jansen-West, K. R., Franco-Mesa, C., Heckman, M. G., White, L. J., Dunmore, J. A., Cook, C. N., Lilley, M. T., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abi7643 · Cited by 78
Work-based predictors of mortality: A 20-year follow-up of healthy employees.
Shirom, A., Toker, S., Alkaly, Y., Jacobson, O., & Balicer, R. D. (2011) · Health Psychology · doi:10.1037/a0023138 · Cited by 78
Harford, T. C., Chen, C. M., Kerridge, B. T., & Grant, B. F. (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.09.012 · Cited by 81
Hund, L., Bedrick, E. J., Miller, C. P., Huerta, G., Nez, T., Ramone, S., Shuey, C., Cajero, M., & Lewis, J. L. (2015) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12099 · Cited by 77
“We do not have ‘pre-exposure’ data on lifestyle factors and controlling for ‘post-exposure’ risk factors can induce bias (Schisterman et al., 2009).”
BackgroundSchöttker, B., Saum, K.-U., Muhlack, D. C., Hoppe, L. K., Holleczek, B., & Brenner, H. (2017) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-017-2266-7 · Cited by 77
A Systematic Statistical Approach to Evaluating Evidence from Observational Studies
Madigan, D., Stang, P., Berlin, J. A., Schuemie, M. J., Overhage, J. M., Suchard, M. A., Dumouchel, B., Hartzema, A. G., & Ryan, P. B. (2013) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115645 · Cited by 78
“Statistical control can sometimes either increase bias or decrease precision without affecting bias and can thereby produce less reliable effect estimates (Schisterman et al. 2009).”
BackgroundMicrovascular endothelial dysfunction is associated with albuminuria
Martens, R. J. H., Houben, A. J. H. M., Kooman, J. P., Berendschot, T. T. J. M., Dagnelie, P. C., Kallen, C., Kroon, A. A., Leunissen, K. M. L., SANDE, F. M. V. D., Schaper, N. C., et al. (2018) · Journal of Hypertension · doi:10.1097/hjh.0000000000001674 · Cited by 75
“Copyright © 2018 Wolters Kluwer Journal of Hypertension endothelial dysfunction with albuminuria in our models may have introduced overadjustment bias [43].”
BackgroundHooker, S. P., Diaz, K. M., Blair, S. N., Colabianchi, N., Hutto, B., McDonnell, M. N., Vena, J. E., & Howard, V. J. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.15385 · Cited by 79
Mebrahtu, T. F., Morgan, A. W., Keeley, A., Baxter, P. D., Stewart, P. M., & Pujades-Rodriguez, M. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2019-00153 · Cited by 75
“3) Erythrocyte sedimentation rate, mm/h 17 (24) 13 (21) Comorbidities, n (%) Asthma 18,217 (25.”
MethodStatistics, Adjusted Statistics, and Maladjusted Statistics
Kaufman, J. S. (2017) · American Journal of Law & Medicine · doi:10.1177/0098858817723659 · Cited by 75
Starling, A. P., Adgate, J. L., Hamman, R. F., Kechris, K. J., Calafat, A. M., & Dabelea, D. (2019) · Environment International · doi:10.1016/j.envint.2019.104983 · Cited by 76
van den Berg, E., Pasch, A., Westendorp, W. H., Navis, G. J., Brink, E. J., Gans, R. O. B., van Goor, H., & Bakker, S. J. L. (2014) · Journal of the American Society of Nephrology · doi:10.1681/asn.2013050497 · Cited by 76
Hagen, K., Åsberg, A. N., Stovner, L. J., Linde, M., Zwart, J., Winsvold, B. S., & Heuch, I. (2018) · Cephalalgia · doi:10.1177/0333102418764888 · Cited by 74
“However, to avoid overadjustment bias (26), we adjusted only for the most established confounding factors in headache epidemiological studies.”
BackgroundJoinson, C. J., Kounali, D., & Lewis, G. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1308-2 · Cited by 79
“Bias can be induced by controlling for intermediate variables on the causal pathway between an exposure and outcome [15, 21].”
BackgroundUsing Design Thinking to Differentiate Useful From Misleading Evidence in Observational Research
Goodman, S. N., Schneeweiß, S., & Baiocchi, M. (2017) · JAMA · doi:10.1001/jama.2016.19970 · Cited by 75
Gray, R. T., Coleman, H. G., Hughes, C. M., Murray, L., & Cardwell, C. R. (2016) · Cancer Epidemiology · doi:10.1016/j.canep.2016.10.004 · Cited by 72
“To avoid overadjustment this analysis did not adjust for stage and grade, or restrict the cohort to Dukes’ stage A–C disease, because these variables could be on the causal pathway for the association between prediagnostic statin use and CRC–specific mortality [31,32].”
BackgroundChan, L. L. Y., Wong, A. Y. L., Wang, M. H., Cheung, K., & Samartzis, D. (2020) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2020.102934 · Cited by 79
Nishijima, T., Kawasaki, Y., Tanaka, N., Mizushima, D., Aoki, T., Watanabe, K., Kinai, E., Honda, H., Yazaki, H., Tanuma, J., et al. (2014) · AIDS · doi:10.1097/qad.0000000000000347 · Cited by 72
“In this case, adding predictive covariates to the logistic regression model will have detrimental effects on precision [31].”
MethodThe influence of interpregnancy interval on infant mortality
McKinney, D. N., House, M. A., Chen, A., Muglia, L. J., & DeFranco, E. (2016) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2016.12.018 · Cited by 72
Estimated GFR, Albuminuria, and Cognitive Performance: The Maastricht Study
Martens, R. J. H., Kooman, J. P., Stehouwer, C. D. A., Dagnelie, P. C., Kallen, C., Koster, A., Kroon, A. A., Leunissen, K. M. L., Nijpels, G., SANDE, F. M. V. D., et al. (2016) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2016.04.017 · Cited by 72
“In addition, it is important to note that, for example, blood pressure and CVDmay also be intermediates in the association between kidney function and cognitive performance, possibly leading to underestimation of the evaluated associations.(41) However, the relatively small attenuation of the regression coefficients a…”
BackgroundMethodAssociation of Repeated Intravitreous Bevacizumab Injections With Risk for Glaucoma Surgery
Eadie, B. D., Etminan, M., Carleton, B., Maberley, D., & Mikelberg, F. S. (2017) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2017.0059 · Cited by 73
Association of Continuation of Loop Diuretics at Hemodialysis Initiation with Clinical Outcomes
Sibbel, S. P., Walker, A. G., Colson, C., Tentori, F., Brunelli, S. M., & Flythe, J. E. (2018) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.05080418 · Cited by 71
“2 Coronary artery disease 677 (11) 566 (11) 20.”
BackgroundNolet, P. S., Côté, P., Kristman, V. L., Rezai, M., Carroll, L., & Cassidy, J. D. (2014) · The Spine Journal · doi:10.1016/j.spinee.2014.12.009 · Cited by 70
Capillary Rarefaction Associates with Albuminuria: The Maastricht Study
Martens, R. J. H., Henry, R. M. A., Houben, A. J. H. M., Kallen, C., Kroon, A. A., Schalkwijk, C. G., Schram, M. T., Sep, S. J. S., Schaper, N. C., Dagnelie, P. C., et al. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015111219 · Cited by 70
Neurological diseases and risk of suicide attempt: a case–control study
Eliasen, A., Dalhoff, K., & Horwitz, H. (2018) · Journal of Neurology · doi:10.1007/s00415-018-8837-4 · Cited by 71
Schliep, K. C., Mclean, H., Yan, B., Qeadan, F., Theilen, L. H., de Havenon, A., Majersik, J. J., Østbye, T., Sharma, S., & Varner, M. W. (2023) · Hypertension · doi:10.1161/hypertensionaha.122.19399 · Cited by 69
Dietary Diversity among Children Aged 6–23 Months in Aleta Wondo District, Southern Ethiopia
Dafursa, K., & Gebremedhin, S. (2019) · Journal of Nutrition and Metabolism · doi:10.1155/2019/2869424 · Cited by 69
“In order to avoid overadjustment bias and unnecessary adjustment, independent variables were fitted into two different distal and proximate models in accordance with the conceptual framework of the study [23].”
MethodTo Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure
Groenwold, R. H. H., Palmer, T., & Tilling, K. M. (2021) · Epidemiology · doi:10.1097/ede.0000000000001312 · Cited by 69
A principled approach to mediation analysis in perinatal epidemiology
Ananth, C. V., & Brandt, J. S. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2021.10.028 · Cited by 69
Sun, J., Zhao, L., Yang, Y., Ma, X., Wang, Y., & Xiang, Y. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv164 · Cited by 74
Pugliese, A., Boulware, D., Yu, L., Babu, S., Steck, A. K., Becker, D., Rodriguez, H., DiMeglio, L., Evans-Molina, C., Harrison, L. C., et al. (2016) · Diabetes · doi:10.2337/db15-1105 · Cited by 68
“(38) elegantly described how unnecessary adjustments could bias results; in this case, adjusting for age and BMI would be an unnecessary adjustment that would misleadingly diminish the impact of DRB1*15:01-DQA1*01:02-DQB1*06:02 positivity.”
BackgroundIob, E., Lacey, R. E., & Steptoe, A. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.07.045 · Cited by 70
“Hence, they were included in the models as additional mediators rather than confounders in order to avoid overadjustment bias (Schisterman et al., 2009).”
BackgroundCumulative stress pathophysiology in schizophrenia as indexed by allostatic load
Nugent, K. L., Chiappelli, J., Rowland, L. M., & Hong, L. E. (2015) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2015.06.009 · Cited by 69
“These results are consistent with prior reports of elevated pulse and altered autonomic functioning in SZ (Van Kammen and Kelley, 1991, Ostermann et al., 2013, Chung et al., 2013; Schisterman et al., 2009).”
ResultRyan, P. B., Schuemie, M. J., Gruber, S., Zorych, I., & Madigan, D. (2013) · Drug Safety · doi:10.1007/s40264-013-0099-6 · Cited by 71
Counterpoint: The Treatment Decision Design
Brookhart, M. A. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv214 · Cited by 67
Impact of Educational Attainment on Health Outcomes in Moderate to Severe CKD
Morton, R. L., Schlackow, I., Staplin, N., Gray, A. M., Cass, A., Haynes, R., Emberson, J., Herrington, W. G., Landray, M., Baigent, C., et al. (2015) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2015.07.021 · Cited by 71
“Models for estimating total effects of education on health outcomes did not adjust for the following variables because they were assumed to be on the causal pathway between educational attainment and the outcomes of interest: cigarette smoking, current alcohol consumption, BMI, comorbid conditions (vascular disease an…”
BackgroundRole of Health Insurance on the Survival of Infants With Congenital Heart Defects
Kucik, J. E., Cassell, C. H., Alverson, C. J., Donohue, P., Tanner, J. P., Minkovitz, C. S., Correia, J. A., Burke, T., & Kirby, R. S. (2014) · American Journal of Public Health · doi:10.2105/ajph.2014.301969 · Cited by 67
“58 Our study improved on previous studies in 4 important areas.”
Riley, E. D., Moore, K., Sorensen, J. L., Tulsky, J. P., Bangsberg, D. R., & Neilands, T. B. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr209 · Cited by 67
Bloom, M. S., Fujimoto, V. Y., Storm, R., Zhang, L., Butts, C. D., Sollohub, D., & Jansing, R. L. (2017) · Reproductive Toxicology · doi:10.1016/j.reprotox.2017.01.004 · Cited by 67
Walkden, G. J., Pickering, A. E., & Gill, H. (2019) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004052 · Cited by 70
Bloom, M. S., Wenzel, A. G., Brock, J. W., Kucklick, J. R., Wineland, R. J., Cruze, L., Unal, E. R., Yucel, R. M., Jiyessova, A., & Newman, R. (2019) · Environment International · doi:10.1016/j.envint.2019.04.005 · Cited by 67
Multidimensional structural racism predicts birth outcomes for Black and White Minnesotans
Chantarat, T., Riper, D. C. V., & Hardeman, R. R. (2022) · Health Services Research · doi:10.1111/1475-6773.13976 · Cited by 71
“tionship between structural racism and adverse birth outcomes(38); including such covariates in the regression model would have biased the estimates to the null.(39) This information is available in Supplement”
Maternal Serum Preconception Polychlorinated Biphenyl Concentrations and Infant Birth Weight
Murphy, L. E., Gollenberg, A., Louis, G. M. B., Kostyniak, P. J., & Sundaram, R. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901150 · Cited by 66
“Such an adjustment has been empirically demonstrated to introduce bias (Schisterman et al. 2009).”
BackgroundSwords, D. S., Mulvihill, S. J., Brooke, B. S., Skarda, D. E., Firpo, M. A., & Scaife, C. L. (2018) · Surgery · doi:10.1016/j.surg.2018.10.035 · Cited by 65
Lower Mortality Associated With Overweight in the U.S. National Health Interview Survey
Wang, Z., Liu, M., Pan, T., & Tong, S. (2016) · Medicine · doi:10.1097/md.0000000000002424 · Cited by 65
Brown, H. K., Cobigo, V., Lunsky, Y., & Vigod, S. N. (2016) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.14120 · Cited by 64
Statin use and breast cancer survival: a nationwide cohort study in Scotland
McMenamin, Ú. C., Murray, L., Hughes, C. M., & Cardwell, C. R. (2016) · BMC Cancer · doi:10.1186/s12885-016-2651-0 · Cited by 64
“An adjusted analysis for pre-diagnostic statin use was first conducted omitting stage, grade, cancer treatment from adjustments for potential confounders to avoid overadjustment [26, 27], as these could be on the causal pathway for breast cancer-specific mortality.”
MethodYang, B., Guo, Y., Bloom, M. S., Xiao, X., Qian, Z. M., Liu, E., Howard, S. W., Zhao, T., Wang, S.-Q., Li, S., et al. (2018) · Environmental Research · doi:10.1016/j.envres.2018.12.047 · Cited by 64
Folkestad, L., Brandt, F., Lillevang‐Johansen, M., Brix, T. H., & Hegedűs, L. D. (2020) · Thyroid · doi:10.1089/thy.2019.0672 · Cited by 63
“It is difficult to adjust for these factors in a statistical analysis as this will result in adjusting the fitted model for the exposure, in this case hyperthyroidism (23).”
Redmond, C., Akinoso-Imran, A. Q., Heaney, L. G., Sheikh, A., Kee, F., & Busby, J. (2021) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2021.10.007 · Cited by 66
Dodge, L. E., Williams, P. L., Williams, M. A., Missmer, S. A., Toth, T. L., Calafat, A. M., & Hauser, R. B. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1408605 · Cited by 64
“When controlling for a mediator, it is possible that the total causal effect of the exposure on the outcome is not consistently estimated; typically, this will bias results toward the null (Schisterman et al. 2009).”
BackgroundExamining Infertility Treatment and Early Childhood Development in the Upstate KIDS Study
Yeung, E. H., Sundaram, R., Bell, E. M., Druschel, C. M., Kus, C. A., Ghassabian, A., Bello, S. C., Xie, Y., & Louis, G. M. B. (2016) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2015.4164 · Cited by 63
Sarkar, R. R., Parsons, J. K., Bryant, A. K., Ryan, S. T., Kader, A. K., McKay, R. R., D'Amico, A. V., Nguyen, P. L., Hulley, B. J., Einck, J. P., et al. (2019) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.0280 · Cited by 63
Dibben, C. J. L., & Clemens, T. L. (2015) · Environmental Research · doi:10.1016/j.envres.2015.05.010 · Cited by 62
“…(which is often used as a surrogate in the absence of adequate individual level information) which would likely result in over-adjustment (Schisterman et al., 2009) for individual and area based variation which is not confounding in this relationship, we restrict our area based effects…”
BackgroundObesity and onset of depression among U.S. middle-aged and older adults
Xiang, X., & An, R. (2014) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2014.12.008 · Cited by 64
“Time-varying covariates were not used in the analyses to avoid overadjustment bias — controlling for an intermediate variable on a causal path from exposure to outcome could bias estimates for the total exposure effect [31].”
MethodBias Amplification and Bias Unmasking
Middleton, J. A., Scott, M. A., Diakow, R., & Hill, J. (2016) · Political Analysis · doi:10.1093/pan/mpw015 · Cited by 61
“Bias inducers include posttreatment variables such as mediators and colliders (Cole et al. 2010; Pearl 2000; Schisterman et al. 2009), and a particular group of pretreatment covariates (pretreatment colliders that lead to M-bias or butterfly-bias) (Ding and Miratrix 2014; Sjlander 2009; Pearl 2009).”
BackgroundArmfield, J. M., Gnanamanickam, E. S., Johnston, D. W., Preen, D. B., Brown, D. S., Nguyen, H., & Segal, L. (2021) · The Lancet Public Health · doi:10.1016/s2468-2667(21)00024-4 · Cited by 61
Duffner, L. A., Deckers, K., Cadar, D., Steptoe, A., de Vugt, M. E., & Köhler, S. (2022) · Epidemiology and Psychiatric Sciences · doi:10.1017/s204579602100069x · Cited by 63
“Hence, the joint model might be over adjustment (Schisterman et al ., 2009).”
del Río, I. D., & del Valle, E. S. G. (2016) · Violence Against Women · doi:10.1177/1077801216671220 · Cited by 61
Wen, S. W., Miao, Q., Taljaard, M., Lougheed, J., Gaudet, L., Davies, M. J., Lanes, A., Leader, A., Corsi, D. J., Sprague, A. E., et al. (2020) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2019.6096 · Cited by 60
Testosterone Therapy and Risk of Myocardial Infarction: A Pharmacoepidemiologic Study
Etminan, M., Skeldon, S., Goldenberg, S. L., Carleton, B., & Brophy, J. M. (2015) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.1534 · Cited by 59
“Causal diagrams were developed to identify true confounding variables and avoid overadjustment bias that may result from adjustment for intermediate variables or colliders (variables that may introduce selection bias).(28) Variables that were risk factors for the outcome, but not for the exposure, were not included in…”
BackgroundPatorno, E., Patrick, A. R., Garry, E. M., Schneeweiß, S., Gillet, V. G., Bartels, D. B., Massó-González, E. L., & Seeger, J. D. (2014) · Diabetologia · doi:10.1007/s00125-014-3364-z · Cited by 60
Variable Selection in Causal Inference using a Simultaneous Penalization Method
Ertefaie, A., Asgharian, M., & Stephens, D. A. (2017) · Journal of Causal Inference · doi:10.1515/jci-2017-0010 · Cited by 61
“However, the typical forfeit for this conservatism is inflation of variance of the effect estimator (Greenland, 2008; Schisterman et al., 2009).”
BackgroundAssessing the VMT effect of ridesourcing services in the US
Wu, X., & MacKenzie, D. (2021) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2021.102816 · Cited by 61
Schisterman, E. F., Gaskins, A. J., Mumford, S. L., Browne, R. W., Yeung, E. H., Trevisan, M., Hediger, M. L., Zhang, C., Perkins, N. J., Hovey, K. M., et al. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq131 · Cited by 59
Self-citation
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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