← Back to Search
A Structural Approach To Selection Bias
M. Hernán, S. Hernández-Díaz, J. Robins
Published 2004 · Psychology, Medicine
Save to my Library
Download via 🐼 PaperPanda Download via oaDOI Download via OAB Download via LibKey Download via Google Google ScholarAnalyze on Scholarcy Visualize in Litmaps
Reduce the time it takes to create your bibliography by a factor of 10 by using the world’s favourite reference manager
Time to take this seriously.
The term “selection bias” encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects (“selection bias”) and those resulting from the existence of common causes of exposure and outcome (“confounding”). This classification also leads to a unified approach to adjust for selection bias.
This paper references
Limitations of the application of fourfold table analysis to hospital data.
J. Berkson (1946)
A Generalization of Sampling Without Replacement from a Finite Universe
D. Horvitz (1952)
An analysis of detection bias and proposed corrections in the study of estrogens and endometrial cancer.
S. Greenland (1981)
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
J. Robins (1986)
A new approach to causal inference in mortality studies with a sustained exposure period—application to the healthy worker survivor effect published errata appear in Mathematical Modelling
JM Robins (1986)
Epidemiology in Medicine
C. Hennekens (1987)
Identifiability and Exchangeability for Direct and Indirect Effects
J. Robins (1992)
Causation, Prediction, and Search. Lecture Notes in Statistics 81
P Spirtes (1993)
Lecture Notes in Statistics
P Spirtes (1993)
Causal diagrams for empirical research
J. Pearl (1995)
Absence of Confounding Does Not Correspond to Collapsibility of the Rate Ratio or Rate Difference
S. Greenland (1996)
Epidemiology. Principles & Methods
B Macmahon (1996)
Epidemiology. Principles & Methods, 2nd ed
B MacMahon (1996)
Philadelphia: WB Saunders Co
L Gordis (1996)
Causal Inference from Complex Longitudinal Data
J. Robins (1997)
Latent Variable Modeling and Applications to Causality
Maia Berkane (1997)
Modern Epidemiology, 2nd ed
KJ Rothman (1998)
Modern Epidemiology, 2nd ed. Philadelphia: Lippincott-Raven
Kj Rothman (1998)
K J Rothman (1998)
Epidemiology: Beyond the Basics
M. Szklo (1999)
Maternal pesticide exposure from multiple sources and selected congenital anomalies.
G. Shaw (1999)
Causal diagrams for epidemiologic research.
S. Greenland (1999)
Marginal Structural Models and Causal Inference in Epidemiology
J. Robins (2000)
Correcting for noncompliance and dependent censoring in an AIDS Clinical Trial with inverse probability of censoring weighted (IPCW) log-rank tests.
J. Robins (2000)
Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.
M. Hernán (2000)
Epidemiology. Beyond the Basics. Gaithersburg, MD: Aspen
M Szklo (2000)
Summary measures of population health
M. T. Molla (2001)
Data, Design, and Background Knowledge in Etiologic Inference
J. Robins (2001)
Causation of Bias: The Episcope
M. Maclure (2001)
Causation of bias: the episcope. Epidemiology
M Maclure (2001)
Data, design, and background knowledge in etiologic infer ence. Epidemiology
JM Robins (2001)
Fallibility in estimating direct effects.
S. Cole (2002)
Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.
M. Hernán (2002)
An overview of relations among causal modelling methods.
S. Greenland (2002)
Causality theory for policy uses of epidemiologic measures Summary Measures of Population Health
S Greenland (2002)
Fallibility in the estimation of direct effects
Causation, Prediction, and Search
T. Burr (2003)
Causality theory for policy uses of epidemiological measures
S. Greenland (2003)
Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
S. Greenland (2003)
Observation and inference. An introduction to the methods of epidemiology
A. Walker (2004)
This paper is referenced by
Intrauterine Exposure to Acetaminophen and Adverse Developmental Outcomes: Epidemiological Findings and Methodological Issues.
Z. Liew (2021)
Reducing survivorship bias due to heterogeneity when comparing treated and controls with a different start of follow up
R. V. Eekelen (2021)
Validation of LexisNexis Accurint in the Georgia Cancer Registry’s Cancer Recurrence and Information Surveillance Program
Kirsten M Woolpert (2021)
Prevalence of avoidable and bias-inflicting methodological pitfalls in real-world studies of medication safety and effectiveness.
K. Bykov (2021)
Identifiability and Estimation Under the Test-negative Design With Population Controls With the Goal of Identifying Risk and Preventive Factors for SARS-CoV-2 Infection.
M. Schnitzer (2021)
Letter to the Editor on: "Effectiveness of Multi-activity, High-intensity Interval Training in School-aged Children."
M. F. T. Mans (2021)
Selection-Bias-Corrected Visualization via Dynamic Reweighting
D. Borland (2021)
Bagged random causal networks for interventional queries on observational biomedical datasets
M. Prosperi (2021)
Physical activity and cardiometabolic health in adolescents with type 2 diabetes: a cross-sectional study
J. Slaght (2021)
Prevalence of clinical‐level emotional/behavioral problems in schoolchildren during the coronavirus disease 2019 pandemic in Japan: A prospective cohort study
Fumito Takahashi (2021)
Intake of n-3 polyunsaturated fatty acids in childhood, FADS genotype and incident asthma
M. Talaei (2021)
The Role of Skin Color in Latino Social Networks: Color Homophily in Sending and Receiving Societies
Wendy D Roth (2021)
Risk of Endometrial Cancer in Women with Diabetes: A Population-Based Retrospective Cohort Study
L. Zabuliene (2021)
Tutorial on Directed Acyclic Graphs.
Jean C Digitale (2021)
Methodological issues in economic evaluations of emergency transport systems in low-income and middle-income countries
R. Lilford (2021)
Prenatal phthalate exposures and executive function in preschool children
Giehae Choi (2021)
Relative 'greenness' and not availability of public open space buffers stressful life events and longitudinal trajectories of psychological distress.
S. Høj (2021)
The Effect of Facility Volume on Survival Following Proctectomy for Rectal Cancer
Vanessa M. Welten (2021)
Causal inference with observational data: the need for triangulation of evidence
G. Hammerton (2021)
Misinformation in and about science
Jevin D. West (2021)
Reflection on modern methods: building causal evidence within high-dimensional molecular epidemiological studies of moderate size.
A. Ponsonby (2021)
Dietary fibers and their effects on health
Mati Ur Rehman Yousafzai (2021)
Cannabis use while trying to conceive: a prospective cohort study evaluating associations with fecundability, live birth and pregnancy loss.
S. Mumford (2021)
Prepregnancy body mass index and spina bifida: Potential contributions of bias
Candice Y Johnson (2021)
Evaluation of the Risk of Stroke Without Anticoagulation Therapy in Men and Women With Atrial Fibrillation Aged 66 to 74 Years Without Other CHA2DS2-VASc Factors.
H. Abdel-Qadir (2021)
Machine Learning in Causal Inference: How do I love thee? Let me count the ways.
L. Balzer (2021)
Commentary on “Predictors of Acute Kidney Injury After Hip Fracture in Older Adults”
J. Christensen (2021)
Obesity and All Cause Mortality Following Acute Coronary Syndrome (ANZACS QI 53).
Michael J.A. Williams (2021)
Reflection on modern methods: causal inference considerations for heterogeneous disease etiology.
D. Nevo (2021)
Early Life Exposure to Food Insecurity is Associated with Changes in BMI During Childhood Among Latinos from CHAMACOS
Ryan J. Gamba (2021)
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
L. L. Folgoc (2021)
The marginal causal effect of opium consumption on the upper gastrointestinal cancer death using parametric g-formula: An analysis of 49,946 cases in the Golestan Cohort Study, Iran
N. Mohammadi (2021)See more