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 avoid improperly adjusting for mediating variables, covariates measured at wave 1 were used instead of wave 2.
“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 · see citing works
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 3,044 · see citing works
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 · see citing works
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · see citing works
Porta, M. (2026) · doi:10.1093/acref/9780197663639.001.0001 · Cited by 2,175 · see citing works
Martuza, V. R., & Kenny, D. A. (1981) · Educational Researcher · doi:10.2307/1175080 · Cited by 2,034 · see citing works
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 · see citing works
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 · see citing works
Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445 · cited 3× · see citing works
“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 1,265 · cited 1× · see citing works
“” 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…”
BackgroundAn introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156 · see citing works
Instruments for Causal Inference: An Epidemiologist's Dream?
Hernán, M. A., & Robins, J. M. (2006) · Epidemiology · doi:10.1097/01.ede.0000222409.00878.37 · Cited by 961 · cited 1× · see citing works
“One may note the similarity of DAG 3 to standard representations of instrumental variables.(21,22) Indeed, in DAG 3, M is an instrument for the effect of intermediate U on outcome D.”
BackgroundQuantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810 · cited 1× · see citing works
“Adjusting for a descending proxy M of an unmeasured intermediate variable U (or U itself, if it were measured), is susceptible to collider-stratification bias.(23) In this instance, the unmeasured common cause V of the proxy variable M and the outcome variable D causes additional bias in the association between exposu…”
BackgroundMethods in Social Epidemiology
Oakes, J. M., & Kaufman, J. S. (2006) · Cited by 773 · see citing works
Fallibility in estimating direct effects
Cole, S. R., & Hernán, M. A. (2002) · International Journal of Epidemiology · doi:10.1093/ije/31.1.163 · Cited by 548 · see citing works
Self-citationLiebetrau, A. M. (1983) · Sage eBooks · doi:10.4135/9781412984942.n7 · Cited by 484 · see citing works
Some Surprising Results about Covariate Adjustment in Logistic Regression Models
Robinson, L. D., & Jewell, N. P. (1991) · International Statistical Review · doi:10.2307/1403444 · Cited by 410 · cited 2× · see citing works
“This has been extensively studied by others.(24) We demonstrated that, even in the linear case, the”
BackgroundToward a Clearer Definition of Confounding
Weinberg, C. R. (1993) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a116591 · Cited by 401 · see citing works
The Risk of Preeclampsia Rises with Increasing Prepregnancy Body Mass Index
Bodnar, L. M., Ness, R. B., Marković, N., & Roberts, J. M. (2005) · Annals of Epidemiology · doi:10.1016/j.annepidem.2004.12.008 · Cited by 395 · see citing works
Confounding in Health Research
Greenland, S., & Morgenstern, H. (2001) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.22.1.189 · Cited by 390 · cited 1× · see citing works
“Estimating the unknown parameters in (1) is equivalent to estimating:”
BackgroundLipid Adjustment in the Analysis of Environmental Contaminants and Human Health Risks
Schisterman, E. F., Whitcomb, B. W., Louis, G. M. B., & Louis, T. A. (2005) · Environmental Health Perspectives · doi:10.1289/ehp.7640 · Cited by 387 · see citing works
Self-citationCONFOUNDING: ESSENCE AND DETECTION1
Miettinen, O. S., & Cook, E. F. (1981) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113225 · Cited by 350 · see citing works
Placental abruption and perinatal mortality in the United States
Ananth, C. V., & Wilcox, A. J. (2001) · American Journal of Epidemiology · doi:10.1093/aje/153.4.332 · Cited by 311 · see citing works
Multiple-imputation for measurement-error correction
Cole, S. R., Chu, H., & Greenland, S. (2006) · International Journal of Epidemiology · doi:10.1093/ije/dyl097 · Cited by 256 · see citing works
Self-citationRegression calibration method for correcting measurement-error bias in nutritional epidemiology
Spiegelman, D. L., McDermott, A., & ROSNER, B. A. (1997) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/65.4.1179s · Cited by 254 · see citing works
Kaufman, J. S., MacLehose, R. F., & Kaufman, S. (2004) · Epidemiologic Perspectives & Innovations · doi:10.1186/1742-5573-1-4 · Cited by 253 · cited 1× · see citing works
“Such estimates are vulnerable to collider-stratification bias or exposure interactions with the intermediate variable.(14) (6) Epidemiology • Volume 20, Number 4, July 2009 Overadjustment in Epidemiologic Studies”
BackgroundTrends in Fetal and Infant Survival Following Preeclampsia
Basso, O., Rasmussen, S., Weinberg, C. R., Wilcox, A. J., Irgens, L. M., & Skjærven, R. (2006) · JAMA · doi:10.1001/jama.296.11.1357 · Cited by 235 · see citing works
The control of confounding by intermediate variables
Robins, J. M. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080608 · Cited by 233 · see citing works
Imputation for Exposure Histories with Gaps, under an Excess Relative Risk Model
Weinberg, C. R., Moledor, E., Umbach, D. M., & Sandler, D. P. (1996) · Epidemiology · doi:10.1097/00001648-199609000-00007 · Cited by 178 · see citing works
Cigarette Smoking and Dementia
Hernán, M. Á., Alonso, Á., & Logroscino, G. (2008) · Epidemiology · doi:10.1097/ede.0b013e31816bbe14 · Cited by 161 · cited 1× · see citing works
“Therefore, the bias arising from using model (2) to estimate the total effect of E on D is”
MethodDesign and Analysis of Case-Control Studies
Breslow, N. E. (1982) · Annual Review of Public Health · doi:10.1146/annurev.pu.03.050182.000333 · Cited by 160 · see citing works
Birth Weight and Mortality: Causality or Confounding?
Basso, O., Wilcox, A. J., & Weinberg, C. R. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj237 · Cited by 131 · cited 1× · see citing works
“Investigators have speculated for decades on possible causes of neonatal mortality, and have consistently demonstrated that birth weight is a strong predictor of neonatal and infant mortality.(28,29) When assessing the effect of possible risk factors for neonatal and infant mortality (eg, maternal smoking,(28) multipl…”
BackgroundAn Introduction To Instrumental Variables For epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/oxfordjournals.ije.a019909 · Cited by 121 · see citing works
Why do small twins have a lower mortality rate than small singletons?
Buekens, P. M., & Wilcox, A. J. (1993) · American Journal of Obstetrics and Gynecology · doi:10.1016/s0002-9378(12)90849-2 · Cited by 100 · see citing works
Trends in Preterm-Related Infant Mortality by Race and Ethnicity, United States, 1999–2004
MacDorman, M. F., Callaghan, W. M., Mathews, T. J., Hoyert, D. L., & Kochanek, K. D. (2007) · International Journal of Health Services · doi:10.2190/hs.37.4.c · Cited by 90 · see citing works
Absence of Confounding Does Not Correspond to Collapsibility of the Rate Ratio or Rate Difference
Greenland, S. (1996) · Epidemiology · doi:10.1097/00001648-199609000-00008 · Cited by 77 · see citing works
Kaufman, S., Kaufman, J. S., MacLehose, R. F., Greenland, S., & Poole, C. (2005) · Statistics in Medicine · doi:10.1002/sim.2057 · Cited by 59 · see citing works
Invited Commentary: Simple Models for a Complicated Reality
Schisterman, E. F., & Hernández–Dı́az, S. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj238 · Cited by 8 · cited 1× · see citing works
“Investigators have speculated for decades on possible causes of neonatal mortality, and have consistently demonstrated that birth weight is a strong predictor of neonatal and infant mortality.(28,29) When assessing the effect of possible risk factors for neonatal and infant mortality (eg, maternal smoking,(28) multipl…”
BackgroundSelf-citationD’Agostino (1998) · doi:10.1002/(sici)1097-0258(19981015)17:19
Flagged first, then by citations
Research works printed but not linked: 1
- [6]Rothman K Greenland S Modern Epidemiology 1998 2nd ed. Philadelphia Lippincott-Raven
Cited by
Sorted by citations
Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission
Livingston, G., Huntley, J., Liu, K. Y., Costafreda, S. G., Selbæk, G., Alladi, S., Ames, D., Banerjee, S., Burns, A., Brayne, C., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)01296-0 · Cited by 3,860 · see citing works
Early Life Exposure to Endocrine Disrupting Chemicals and Childhood Obesity and Neurodevelopment
Braun, J. M. (2016) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2016.186 · Cited by 837 · see citing works
Illustrating bias due to conditioning on a collider
Cole, S. R., Platt, R. W., Schisterman, E. F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyp334 · Cited by 796 · see citing works
Self-citationThe impact of childhood obesity on morbidity and mortality in adulthood: a systematic review
Park, M. H., Falconer, C., Viner, R., & Kinra, S. (2012) · Obesity Reviews · doi:10.1111/j.1467-789x.2012.01015.x · Cited by 739 · see citing works
Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., et al. (2019) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201808-564ps · Cited by 715 · see citing works
Propensity Score Methods for Confounding Control in Nonexperimental Research
Brookhart, M. A., Wyss, R., Layton, J. B., & Stürmer, T. (2013) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.113.000359 · Cited by 590 · see citing works
“If the cause of the large weights is unmeasured confounding, however, trimming may decrease bias.(39) In this case, the disadvantage of losing the causal interpretation would be moot because generalizability is no longer relevant in the setting of a biased estimator.”
BackgroundRiehm, K. E., Feder, K. A., Tormohlen, K. N., Crum, R. M., Young, A. S., Green, K. M., Pacek, L. R., La Flair, L. N., & Mojtabai, R. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.2325 · Cited by 511 · see citing works
Grinsztejn, B. G. J., Hosseinipour, M. C., Ribaudo, H. J., Swindells, S., Eron, J. J., Chen, Y. Q., Wang, L., Ou, S., Anderson, M., McCauley, M., et al. (2014) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(13)70692-3 · Cited by 494 · see citing works
Inouye, S. K., Marcantonio, E. R., Kosar, C. M., Tommet, D., Schmitt, E. M., Travison, T. G., Saczynski, J. S., Ngo, L. H., Alsop, D. C., & Jones, R. N. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.03.005 · Cited by 462 · see citing works
“Because we wanted to adjust for baseline covariables yet avoid overcontrolling for variables that might be intermediaries between delirium and its effect on cognitive trajectory [41,42], we did not adjust for hospital-related factors.”
BackgroundAfilalo, J., Mottillo, S., Eisenberg, M. J., Alexander, K. P., Noiseux, N., Perrault, L. P., Morin, J., Langlois, Y., Ohayon, S. M., Monette, J., et al. (2012) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.111.963157 · Cited by 452 · see citing works
A New Criterion for Confounder Selection
VanderWeele, T. J., & Shpitser, I. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01619.x · Cited by 434 · see citing works
“It is well known that control for such variables tends to increase the standard error of the estimates of treatment effects (e.g., Robinson and Jewell, 1991; Schisterman, Cole, and Platt, 2009).”
BackgroundActive-comparator design and new-user design in observational studies
Yoshida, K., Solomon, D. H., & Kim, S. C. (2015) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2015.30 · Cited by 411 · see citing works
Health Literacy and Health Outcomes in Diabetes: A Systematic Review
Sayah, F. A., Majumdar, S. R., Williams, B. A., Robertson, S. L., & Johnson, J. A. (2012) · Journal of General Internal Medicine · doi:10.1007/s11606-012-2241-z · Cited by 387 · see citing works
Simmonds, M. C., Burch, J., Llewellyn, A., Griffiths, C., Yang, H., Owen, C. G., Duffy, S., & Woolacott, N. F. (2015) · Health Technology Assessment · doi:10.3310/hta19430 · Cited by 384 · see citing works
Confounding Control in Healthcare Database Research
Brookhart, M. A., Stürmer, T., Glynn, R. J., Rassen, J. A., & Schneeweiß, S. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181dbebe3 · Cited by 390 · see citing works
Rai, D., Lee, B. K., Dalman, C., Golding, J., Lewis, G., & Magnusson, C. (2013) · BMJ · doi:10.1136/bmj.f2059 · Cited by 365 · see citing works
Nardone, A., Rudolph, K. E., Morello-Frosch, R. A., & Casey, J. A. (2021) · Environmental Health Perspectives · doi:10.1289/ehp7495 · Cited by 365 · see citing works
“…include present-day sociodemographic measures in our analyses because HOLC grades reduced home ownership, investment, and degraded wealth-generating opportunities in redlined and yellow-lined communities, thus making present-day demographic variables potential mediators (Schisterman et al. 2009).”
Handling time varying confounding in observational research
Mansournia, M. A., Etminan, M., Danaei, G., Kaufman, J. S., & Collins, P. G. S. (2017) · BMJ · doi:10.1136/bmj.j4587 · Cited by 343 · see citing works
Kandola, A. A., Lewis, G., Osborn, D., Stubbs, B., & Hayes, J. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30034-1 · Cited by 348 · see citing works
Braun, J. M., Kalkbrenner, A. E., Just, A. C., Yolton, K., Calafat, A. M., Sjödin, A., Hauser, R., Webster, G. M., Chen, A., & Lanphear, B. P. (2014) · Environmental Health Perspectives · doi:10.1289/ehp.1307261 · Cited by 328 · see citing works
Ananth, C. V., & Schisterman, E. F. (2017) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2017.04.016 · Cited by 324 · see citing works
Self-citationAdjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies
Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L., & Kraft, P. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.12.021 · Cited by 287 · see citing works
“Here, classical epidemiologists advise against adjustment for C, since C is in the causal pathway and hence not a confounder.(12,13) In contrast, instances can be found where genetic epidemiologists seek the controlled direct causal effect of G on Y by noting their intent to identify variants associated with the outco…”
BackgroundBerkson's Bias, Selection Bias, and Missing Data
Westreich, D. J. (2011) · Epidemiology · doi:10.1097/ede.0b013e31823b6296 · Cited by 323 · see citing works
Sullivan, S. G., Tchetgen, E. J. T., & Cowling, B. J. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kww064 · Cited by 316 · see citing works
When and how can real world data analyses substitute for randomized controlled trials?
Franklin, J. M., & Schneeweiss, S. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.857 · Cited by 316 · see citing works
“We also want to avoid adjusting for variables on the causal pathway between treatment and outcome because this adjustment can result in biased estimation of both the total effect of treatment and the direct effect that is not mediated through the adjustment variables.(49) A common mistake of this type in comparative e…”
MethodHill, A., Pallitto, C., McCleary‐Sills, J., & Garcia‐Moreno, C. (2016) · International Journal of Gynecology & Obstetrics · doi:10.1016/j.ijgo.2015.10.023 · Cited by 314 · see citing works
Metrics for covariate balance in cohort studies of causal effects
Franklin, J., Rassen, J. A., Ackermann, D., Bartels, D., & Schneeweiss, S. (2014) · Statistics in Medicine · doi:10.1002/sim.6058 · Cited by 316 · see citing works
Patrick, S. W., Fry, C., Jones, T. F., & Buntin, M. B. (2016) · Health Affairs · doi:10.1377/hlthaff.2015.1496 · Cited by 301 · see citing works
Chandrabose, M., Rachele, J. N., Gunn, L., Kavanagh, A. M., Owen, N. G., Turrell, G., Giles‐Corti, B., & Sugiyama, T. (2018) · Obesity Reviews · doi:10.1111/obr.12759 · Cited by 276 · see citing works
du Fossé, N. A., van der Hoorn, M.-L. P., van Lith, J. M. M., le Cessie, S., & Lashley, E. E. L. O. (2020) · Human Reproduction Update · doi:10.1093/humupd/dmaa010 · Cited by 276 · see citing works
Luckhaupt, S. E., Tak, S., & Calvert, G. M. (2010) · SLEEP · doi:10.1093/sleep/33.2.149 · Cited by 272 · see citing works
Gan, W., Koehoorn, M. W., Davies, H., Demers, P. A., Tamburic, L., & Brauer, M. D. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.1002511 · Cited by 263 · see citing works
“On the other hand, because diabetes, COPD, and hypertensive heart disease might serve as intermediate variables for the association between traffic-related air pollution and coronary events, adjusting for these comorbidities might lead to underestimations of the true adverse effects (Schisterman et al. 2009).”
Not all screen time is created equal: associations with mental health vary by activity and gender
Twenge, J. M., & Farley, E. (2021) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-020-01906-9 · Cited by 252 · see citing works
Patients With Ankylosing Spondylitis Have Increased Cardiovascular and Cerebrovascular Mortality
Haroon, N. N., Paterson, J. M., Li, P., Inman, R. D., & Haroon, N. (2015) · Annals of Internal Medicine · doi:10.7326/m14-2470 · Cited by 261 · see citing works
“If we consider vascular morbidity to be directly linked to the AS disease process, by excluding patients with baseline vascular morbidity, we would have overadjusted in the analysis (47).”
BackgroundGan, W., Davies, H., Koehoorn, M. W., & Brauer, M. D. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kwr424 · Cited by 257 · see citing works
“Because these comorbid conditions andCHDshare commonbehavioral risk factors, the adjustmentwas able to reduce the influences of unmeasured risk factors and these conditions themselveson the effect estimates to some extent (24); on the other hand, because these conditions might serve as intermediate variables for the a…”
BackgroundAntidepressant Use Late in Pregnancy and Risk of Persistent Pulmonary Hypertension of the Newborn
Huybrechts, K. F., Bateman, B. T., Palmsten, K. K., Desai, R. J., Patorno, E., Gopalakrishnan, C., Levin, R., Mogun, H., & Hernández–Dı́az, S. (2015) · JAMA · doi:10.1001/jama.2015.5605 · Cited by 254 · see citing works
A basic study design for expedited safety signal evaluation based on electronic healthcare data
Schneeweiß, S. (2010) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1926 · Cited by 251 · see citing works
“The clear temporal sequence of confounder adjustment before treatment initiation in an incident user design avoids mistakenly adjusting for consequences of treatment (intermediates) rather than predictors for treatment, a possible reason for overadjustment.(12) Identifying two active treatment groups further reduces t…”
BackgroundPrediabetes and Type 2 Diabetes Are Associated With Generalized Microvascular Dysfunction
Sörensen, B. M., Houben, A. J. H. M., Berendschot, T. T. J. M., Schouten, J., Kroon, A. A., Kallen, C., Henry, R. M. A., Koster, A., Sep, S. J. S., Dagnelie, P. C., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.023446 · Cited by 246 · see citing works
Food Insecurity and Eating Disorders: a Review of Emerging Evidence
Hazzard, V. M., Loth, K. A., Hooper, L., & Becker, C. B. (2020) · Current Psychiatry Reports · doi:10.1007/s11920-020-01200-0 · Cited by 248 · see citing works
Urinary bisphenol A concentrations and early reproductive health outcomes among women undergoing IVF
Ehrlich, S., Williams, P. L., Missmer, S. A., Flaws, J. A., Ye, X., Calafat, A. M., Petrozza, J. C., Wright, D. L., & Hauser, R. B. (2012) · Human Reproduction · doi:10.1093/humrep/des328 · Cited by 246 · see citing works
“Also, we would expect the association between urinary BPA and peak serum E2 to be attenuated when the intermediate variable was included in the multivariate analyses, because of overadjustment (Schisterman et al., 2009).”
BackgroundXie, Y., Szeto, G. P. Y., & Dai, J. (2016) · Applied Ergonomics · doi:10.1016/j.apergo.2016.08.020 · Cited by 258 · see citing works
“Although confounder adjustment is an important method to minimize bias, unnecessary or over adjustment could result in negative rather than positive effects on the precision of results (Schisterman et al., 2009).”
MethodMeaningful associations in the adolescent brain cognitive development study
Dick, A. S., Lopez, D. A., Watts, A. L., Heeringa, S. G., Reuter, C., Bartsch, H., Fan, C. C., Kennedy, D. N., Palmer, C. E., Marshall, A. T., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118262 · Cited by 234 · see citing works
“We caution against the over-inclusion of covariates n statistical models, and against the assumption that including coariates purifies the associations among our variables of interest; intead their inclusion can obscure rather than purify such associations Schisterman et al., 2009 ).”
Buckley, J. P., Samet, J. M., & Richardson, D. B. (2014) · Epidemiology · doi:10.1097/ede.0000000000000051 · Cited by 237 · see citing works
Nín, N., Muriel, A., Peñuelas, Ó., Brochard, L. J., Lorente, J. Á., Ferguson, N. D., Raymondos, K., Ríos, F., Violi, D. A., Thille, A. W., et al. (2017) · Intensive Care Medicine · doi:10.1007/s00134-016-4611-1 · Cited by 233 · see citing works
“More recent studies have reported that high levels of PaCO2 activate specific signaling pathways, independently of pH or reactive oxygen species, leading to impaired lung function [19, 33, 34].”
BackgroundTime series regression model for infectious disease and weather
Imai, C., Armstrong, B., Chalabi, Z., Mangtani, P., & Hashizume, M. (2015) · Environmental Research · doi:10.1016/j.envres.2015.06.040 · Cited by 230 · see citing works
“The inclusion of past cases would cause downward bias (i.e. overadjustment (Schisterman et al., 2009)) in the total effect of a longer lagged weather exposure if part of the effect was through a causal path involving a more immediate impact of that exposure causing higher counts and hence…”
BackgroundInfluentialGérardin, P., Sampériz, S., Ramful, D., Boumahni, B., Bintner, M., Alessandri, J., Carbonnier, M., Tiran-Rajaofera, I., Beullier, G., Boya, I., et al. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002996 · Cited by 229 · see citing works
“Comparisons in two-by-two adjustments and in GEE-logistic models between crude and adjusted odd ratios (OR) of p-CHIKV infection and between the variations of their standard errors (SE) allowed to distinguish confounding and over-adjustment [18,19].”
BackgroundExtracorporeal cardiopulmonary resuscitation for cardiac arrest: A systematic review
Holmberg, M. J., Geri, G., Wiberg, S., Guerguerian, A.-M., Donnino, M. W., Nolan, J. P., Deakin, C. D., & Andersen, L. W. (2018) · Resuscitation · doi:10.1016/j.resuscitation.2018.07.029 · Cited by 250 · see citing works
“Adjusting for “CPR duration” using traditional methods is therefore likely to introduce biased results, although the direction of this bias can be difficult to predict [47].”
BackgroundMethodThe impact of diabetes on the pathogenesis of sepsis
Koh, G. C. K. W., Peacock, S. J., van der Poll, T., & Wiersinga, W. J. (2011) · European Journal of Clinical Microbiology & Infectious Diseases · doi:10.1007/s10096-011-1337-4 · Cited by 226 · see citing works
“or spurious results [40].”
Outcome-Adaptive Lasso: Variable Selection for Causal Inference
Shortreed, S. M., & Ertefaie, A. (2017) · Biometrics · doi:10.1111/biom.12679 · Cited by 219 · see citing works
“While incorporating all confounders is important for unbiased treatment effect estimates, recent work has shown that efficiency losses can accompany the inclusion of extraneous variables (Greenland, 2008; Schisterman et al., 2009; Rotnitzky et al., 2010; Myers et al., 2011; Patrick et al., 2011).”
BackgroundSathiakumar, N., Delzell, E., Morrisey, M. A., Falkson, C., Yong, M., Chia, V., Blackburn, J. L., Arora, T., & Kilgore, M. L. (2011) · Prostate Cancer and Prostatic Diseases · doi:10.1038/pcan.2011.7 · Cited by 219 · see citing works
Association Between Prepregnancy Body Mass Index and Severe Maternal Morbidity
Lisonkova, S., Muraca, G. M., Potts, J., Liauw, J., Chan, W.-S., Skoll, A., & Lim, K. I. (2017) · JAMA · doi:10.1001/jama.2017.16191 · Cited by 210 · see citing works
The Great Recession, unemployment and suicide
Norström, T., & Grönqvist, H. (2014) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2014-204602 · Cited by 191 · see citing works
Flohr, C., Perkin, M. R., Logan, K., Marrs, T., Radulović, S., Campbell, L. E., MacCallum, S. F., McLean, W. H. I., & Lack, G. (2013) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.298 · Cited by 192 · see citing works
“, AD phenotype and severity) and some others potentially on a causal pathway (TEWL is likely to be on the causal pathway between FLG mutation carriage and AD development), we specifically looked for evidence of collinearity influencing the statistical analyses (Schisterman et al., 2009).”
BackgroundThe Parametric g-Formula for Time-to-event Data
Keil, A. P., Edwards, J. K., Richardson, D. B., Naimi, A. I., & Cole, S. R. (2014) · Epidemiology · doi:10.1097/ede.0000000000000160 · Cited by 190 · see citing works
Self-citationTwenge, J. M., Haidt, J., Lozano, J., & Cummins, K. M. (2022) · Acta Psychologica · doi:10.1016/j.actpsy.2022.103512 · Cited by 191 · see citing works
Is Nature Relatedness Associated with Better Mental and Physical Health?
Dean, J., Shanahan, D. F., Bush, R. A. B., Gaston, K. J., Lin, B. B., Barber, E., Franco, L., & Fuller, R. A. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15071371 · Cited by 190 · see citing works
“This issue arises where covariates are included in the models that are on the causal pathway from an influencing factor to an effect [92].”
BackgroundUnderestimating digital media harm
Twenge, J. M., Haidt, J., Joiner, T. E., & Campbell, W. K. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0839-4 · Cited by 188 · see citing works
Maternal exposure to perfluorinated chemicals and reduced fecundity: the MIREC study
Vélez, M. P., Arbuckle, T. E., & Fraser, W. D. (2015) · Human Reproduction · doi:10.1093/humrep/deu350 · Cited by 187 · see citing works
“Conditioning on a collider is susceptible to collider-stratification bias (Greenland, 2003; Schisterman et al., 2009).”
BackgroundInfluentialReview: the association between anxiety and poor attendance at school – a systematic review
Finning, K., Ukoumunne, O. C., Ford, T., Danielson‐Waters, E., Shaw, L., Romero De Jager, I., Stentiford, L., & Moore, D. A. (2019) · Child and Adolescent Mental Health · doi:10.1111/camh.12322 · Cited by 189 · see citing works
Lazarevic, N., Barnett, A. G., Sly, P. D., & Knibbs, L. D. (2019) · Environmental Health Perspectives · doi:10.1289/ehp2207 · Cited by 184 · see citing works
“Additionally, prenatal e ff ect estimates may be subject to overad-justment bias if postnatal exposure is on the causal pathway between prenatal exposure and the outcome (Schisterman et al. 2009), which may be the case for persistent chemicals.”
Sullivan, S. G., Feng, S., & Cowling, B. J. (2014) · Expert Review of Vaccines · doi:10.1586/14760584.2014.966695 · Cited by 184 · see citing works
Association of Obesity With Mortality Over 24 Years of Weight History
Xu, H., Cupples, L. A., Stokes, A., & Liu, C. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.4587 · Cited by 181 · see citing works
Thrift, A. P., Shaheen, N. J., Gammon, M. D., Bernstein, L., Reid, B. J., Onstad, L., Risch, H. A., Liu, G., Bird, N. C., Wu, A. H., et al. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju252 · Cited by 179 · see citing works
“Firstly, GER may be an intermediary on the causal pathway between obesity and neoplasia, in which case adjusting for GER symptoms may actually bias the true risk estimate (34).”
BackgroundSullivan, K. A., Messer, L. C., & Quinlivan, E. B. (2015) · AIDS Patient Care and STDs · doi:10.1089/apc.2014.0278 · Cited by 174 · see citing works
“Adjusting for causal intermediates is inappropriate,(34) and their inclusion would result in an overadjustment bias.(35) However, we did include the length of time participants had been diagnosed with HIV as a covariate in the adjusted models.”
BackgroundStatin Use After Colorectal Cancer Diagnosis and Survival: A Population-Based Cohort Study
Cardwell, C. R., Hicks, B. M., Hughes, C. M., & Murray, L. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.54.4569 · Cited by 177 · see citing works
Usami, S. (2020) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2020.1821690 · Cited by 168 · see citing works
“See Schisterman et al. (2009) regarding this point.”
Freire, C., Amaya, E., Gil, F., Fernández, M. F., Murcia, M., Llop, S., Andiarena, A., Aurrekoetxea, J., Bustamante, M., Guxens, M., et al. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.11.273 · Cited by 172 · see citing works
Do more robust prescription drug monitoring programs reduce prescription opioid overdose?
Pardo, B. (2016) · Addiction · doi:10.1111/add.13741 · Cited by 174 · see citing works
On model selection and model misspecification in causal inference.
Vansteelandt, S., Bekaert, M., & Claeskens, G. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210387717 · Cited by 172 · see citing works
Bellettiere, J., Winkler, E. A. H., Chastin, S. F. M., Kerr, J., Owen, N., Dunstan, D. W., & Healy, G. N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0180119 · Cited by 169 · see citing works
“Models do not adjust for sitting time as a confounder [44] because increasing the volume of sitting is one of the ways in which sitting for long periods may impact biomarkers.”
BackgroundChronic health conditions and school performance among children and youth
Crump, C., Rivera, D. A., London, R. A., Landau, M., Erlendson, B., & Rodriguez, E. (2013) · Annals of Epidemiology · doi:10.1016/j.annepidem.2013.01.001 · Cited by 169 · see citing works
“Adjustment for an intermediate variable such as absenteeism can provide valid estimates of a direct causal effect of chronic health conditions on low school performance under certain assumptions, namely, the absence of unmeasured confounding in the associations between absenteeism or chronic health conditions and low…”
BackgroundNieminen, P. A., Lehtiniemi, H., Vähäkangas, K., Huusko, A., & Rautio, A. (2022) · Epidemiology Biostatistics and Public Health · doi:10.2427/8854 · Cited by 169 · see citing works
“Chemosphere 2005; 61(3): 383-8 [25] Schisterman EF, Cole SR, Platt RW.”
BackgroundHostinar, C. E., Lachman, M. E., Mroczek, D. K., Seeman, T. E., & Miller, G. E. (2015) · Developmental Psychology · doi:10.1037/dev0000049 · Cited by 167 · see citing works
Nondepolarizing Neuromuscular Blocking Agents, Reversal, and Risk of Postoperative Pneumonia
Bulka, C. M., Terekhov, M. A., Martin, B. J., Dmochowski, R. R., Hayes, R. M., & Ehrenfeld, J. M. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001279 · Cited by 165 · see citing works
Mirrahimi, A., de Souza, R. J., Chiavaroli, L., Sievenpiper, J. L., Beyene, J., Hanley, A. J., Augustin, L. S. A., Kendall, C. W. C., & Jenkins, D. J. A. (2012) · Journal of the American Heart Association · doi:10.1161/jaha.112.000752 · Cited by 161 · see citing works
“Another inherent limitation of observational analyses is the potential problem for residual confounding as well as the possibility of overadjusting, which remains an area of debate in epidemiology.(63) Perhaps, the most common limitation in meta-analyses of dietary studies is the combination of dietary data collected…”
BackgroundMatched designs and causal diagrams
Mansournia, M. A., Hernán, M. Á., & Greenland, S. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt083 · Cited by 161 · see citing works
Are socio-economic disparities in diet quality explained by diet cost?
Monsivais, P., Aggarwal, A., & Drewnowski, A. (2010) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2010.122333 · Cited by 161 · see citing works
“A mediating variable, by definition, explains at least some of the observed association between an exposure (eg, indicators of SEP) and an outcome (eg, nutrient density of the diet),(44) and, when controlled for statistically through adjustment (shown in figure 2), biases the main exposureeoutcome association towards…”
BackgroundHo, F. K. W., Gray, S. R., Welsh, P. I., Petermann-Rocha, F. E., Foster, H. M. E., Waddell, H., Anderson, J. J., Lyall, D. M., Sattar, N. A., Gill, J. M. R., et al. (2020) · BMJ · doi:10.1136/bmj.m688 · Cited by 161 · see citing works
McDowell, P. J., McDowell, R., Busby, J., Eastwood, M. C., Patel, P. H., Jackson, D. J., Mansur, A. H., Patel, M., Burhan, H., Doe, S. J., et al. (2023) · European Respiratory Journal · doi:10.1183/13993003.00819-2023 · Cited by 160 · see citing works
“We chose this limited set of adjustment variables to prevent overadjustment bias, in which adjustment is made for variables that lie on the causal path between the exposure and outcome [19, 20].”
MethodMediterranean diet and mobility decline in older persons
Milaneschi, Y., Bandinelli, S., Corsi, A. M., Lauretani, F., Paolisso, G., Dominguez, L. J., Semba, R. D., Tanaka, T., Abbatecola, A. M., Talegawkar, S. A., et al. (2010) · Experimental Gerontology · doi:10.1016/j.exger.2010.11.030 · Cited by 153 · see citing works
“This adjustment was not included in previous analyses in order to avoid overadjustment, defined as the control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome [24].”
BackgroundMethodOpioid use for noncancer pain and risk of myocardial infarction amongst adults
Li, L., Setoguchi, S., Cabral, H., & Jick, S. S. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12035 · Cited by 157 · see citing works
Evaluating the Use of Nonrandomized Real‐World Data Analyses for Regulatory Decision Making
Franklin, J. M., Glynn, R. J., Martin, D., & Schneeweiß, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1351 · Cited by 155 · see citing works
“As in RWE studies with internal control groups, the identification of external control groups should also ensure that covariates are measured prior to the start of exposure to an existing treatment to avoid adjusting for causal intermediaries.(35) Finally, design of the external control group should acknowledge the im…”
BackgroundRisk Factors for Young-Onset Dementia in the UK Biobank
Hendriks, S., Ranson, J. M., Peetoom, K. K. B., Lourida, I., Tai, X. Y., de Vugt, M. E., Llewellyn, D. J., & Köhler, S. (2023) · JAMA Neurology · doi:10.1001/jamaneurol.2023.4929 · Cited by 153 · see citing works
Farmer, R. E., Mathur, R., Schmidt, A. F., Bhaskaran, K., Fatemifar, G., Eastwood, S. V., Finan, C., Denaxas, S., Smeeth, L., & Chaturvedi, N. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.118.011638 · Cited by 144 · see citing works
“While blood pressure and cholesterol are strong predictors of CVD incidence, it is likely that these 2 variables are at least partly on the causal pathway between body composition and the outcomes or share common risk factors with our main exposures, making adjustment for them as “confounders” inappropriate and potent…”
MethodJohns, L. E., Ferguson, K. K., Soldin, O. P., Cantonwine, D. E., Rivera-González, L. O., Toro, L. V. A. D., Calafat, A. M., Ye, X., Alshawabkeh, A. N., Cordero, J. F., et al. (2015) · Reproductive Biology and Endocrinology · doi:10.1186/1477-7827-13-4 · Cited by 152 · see citing works
“However, we have no reason to expect that these would be associated with phthalate exposure, so any deficiencies in these substances would affect the precision of the effect estimates rather than the effect estimates themselves [76].”
BackgroundRace, APOL1 Risk, and eGFR Decline in the General Population
Grams, M. E., Rebholz, C. M., Chen, Y., Rawlings, A. M., Estrella, M. M., Selvin, E., Appel, L. J., Tin, A., & Coresh, J. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015070763 · Cited by 152 · see citing works
Song, X., Jousilahti, P., Stehouwer, C., Söderberg, S., Onat, A., Laatikainen, T., Yudkin, J., Dankner, R., Morris, R., Tuomilehto, J., et al. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.203 · Cited by 148 · see citing works
Endocrine disrupting chemicals and endometriosis
Smarr, M. M., Kannan, K., & Louis, G. M. B. (2016) · Fertility and Sterility · doi:10.1016/j.fertnstert.2016.06.034 · Cited by 149 · see citing works
Duchowny, K. A., Ackley, S. F., Brenowitz, W. D., Wang, J., Zimmerman, S. C., Caunca, M. R., & Glymour, M. M. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.18314 · Cited by 151 · see citing works
The health consequences of child marriage: a systematic review of the evidence
Fan, S., & Koski, A. D. (2022) · BMC Public Health · doi:10.1186/s12889-022-12707-x · Cited by 147 · see citing works
Irvin, M. R., Booth, J. N., Shimbo, D., Lackland, D. T., Oparil, S., Howard, G., Safford, M. M., Muntner, P., & Calhoun, D. A. (2014) · Journal of the American Society of Hypertension · doi:10.1016/j.jash.2014.03.003 · Cited by 146 · see citing works
van Sloten, T. T., Sigurdsson, S. O., van Buchem, M. A., Phillips, C. L., Jónsson, P. V., Ding, J., Schram, M. T., Harris, T. B., Gudnason, V. G., & Launer, L. J. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14050578 · Cited by 146 · see citing works
“We did not adjust the results for total disease loadbecause total disease load includes the load per investigated region (as indicated by the composite scores) and, thus, can be considered an overadjustment (32).”
BackgroundMothers’ Adverse Childhood Experiences and Their Young Children’s Development
Sun, J., Patel, F., Jacobs, R. R., Frank, D. A., Black, M. M., & Chilton, M. M. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.07.015 · Cited by 149 · see citing works
Vasunilashorn, S. M., Dillon, S. T., Inouye, S. K., Ngo, L. H., Fong, T. G., Jones, R. N., Travison, T. G., Schmitt, E. M., Alsop, D. C., Freedman, S. D., et al. (2017) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14913 · Cited by 145 · see citing works
“Charlson Comorbidity Index, n (%) 0 257 (46) 54 (40) 203 (48) 1 139 (25) 23 (17) 116 (27) 2 97 (17) 38 (29) 59 (14) ≥3 67 (12) 19 (14) 48 (11) Vascular comorbidity, n (%) 214 (38) 60 (45) 154 (36)”
"Toward a Clearer Definition of Confounding" Revisited With Directed Acyclic Graphs
Howards, P. P., Schisterman, E. F., Poole, C., Kaufman, J. S., & Weinberg, C. R. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws127 · Cited by 143 · see citing works
Self-citationChiu, Y., Afeiche, M. C., Gaskins, A. J., Williams, P. L., Petrozza, J. C., Tanrikut, C., Hauser, R. B., & Chavarro, J. E. (2015) · Human Reproduction · doi:10.1093/humrep/dev064 · Cited by 143 · see citing works
“Specifically, abstinence time was included regardless of statistical significance since this is a well-known predictor of most semen quality parameters thus helping to reduce the amount of unexplained random variability in the model (Schisterman et al., 2009).”
BackgroundLauritzen, H. B., Larose, T. L., Øien, T., Sandanger, T. M., Odland, J. Ø., van de Bor, M., & Jacobsen, G. W. (2018) · Environmental Health · doi:10.1186/s12940-017-0338-x · Cited by 142 · see citing works
“As adjustment for a mediator may introduce collider bias if there are shared unmeasured causes of both the mediator (SGA status) and the outcome (childhood overweight) [36], we did not include prenatal growth or SGA status in the multivariate analyses.”
BackgroundAssociations between historical redlining and birth outcomes from 2006 through 2015 in California
Nardone, A. L., Casey, J. A., Rudolph, K. E., Karasek, D. A., Mujahid, M. S., & Morello-Frosch, R. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237241 · Cited by 142 · see citing works
Adversity exposure during sensitive periods predicts accelerated epigenetic aging in children
Marini, S., Davis, K. A., Soare, T. W., Zhu, Y., Suderman, M. J., Simpkin, A. J., Smith, A. D. A. C., Wolf, E. J., Relton, C. L., & Dunn, E. C. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.104484 · Cited by 140 · see citing works
Pesticide use and incident diabetes among wives of farmers in the Agricultural Health Study
Starling, A. P., Umbach, D. M., Kamel, F., Long, S., Sandler, D. P., & Hoppin, J. A. (2014) · Occupational and Environmental Medicine · doi:10.1136/oemed-2013-101659 · Cited by 138 · see citing works
“In this scenario, adjustment for body mass index could bias the effect estimate, typically (though not always) toward the null[28].”
Background
- 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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