Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Greenland, G. Maldonado (1994)
Inference on collapsibility in generalized linear modelsBiometrical Journal, 36
J. Pearl (1995)
Causal diagrams for empirical researchBiometrika, 82
E. Halloran, D. Berry (2000)
Statistical models in epidemiology, the environment, and clinical trials
(1980)
Methods in Cancer Research Volume 1: Analysis of CaseControl Data. Geneva, Switzerland: World Health Organization
J. Robins, S. Greenland (1992)
Identifiability and Exchangeability for Direct and Indirect EffectsEpidemiology, 3
D. Rubin (1974)
Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of Educational Psychology, 66
S. Greenland (2001)
Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty AssessmentRisk Analysis, 21
S. Greenland, R. Mickey (1988)
Closed form and dually consistent methods for inference on strict collapsibility in 2×2×K and 2×J×K tablesApplied statistics, 37
G. Maldonado, S. Greenland (2002)
Estimating causal effects.International journal of epidemiology, 31 2
Illtyd Trethowan (1938)
CausalityThe Downside Review, 56
Priya Wickramaratne, Theodore Holford (1987)
Confounding in epidemiologic studies: the adequacy of the control group as a measure of confounding.Biometrics, 43 4
M. Gail, S. Wacholder, J. Lubin (1988)
Indirect corrections for confounding under multiplicative and additive risk models.American journal of industrial medicine, 13 1
C. Rouquette (1970)
[Epidemiologic research].Bulletin de l'Institut national de la sante et de la recherche medicale, 25 4
S. Greenland, J. Robins (1986)
Identifiability, exchangeability, and epidemiological confounding.International journal of epidemiology, 15 3
S. Greenland, H. Morgenstern (2001)
Confounding in health research.Annual review of public health, 22
J. Robins (2000)
Marginal Structural Models versus Structural nested Models as Tools for Causal inference, 116
S. Greenland (2003)
The Impact of Prior Distributions for Uncontrolled Confounding and Response BiasJournal of the American Statistical Association, 98
J. Robins (1989)
The control of confounding by intermediate variables.Statistics in medicine, 8 6
D. Rubin (1978)
Bayesian Inference for Causal Effects: The Role of RandomizationAnnals of Statistics, 6
K. Hoffmann, T. Pischon, M. Schulz, M. Schulze, J. Ray, H. Boeing (2007)
A statistical test for the equality of differently adjusted incidence rate ratios.American journal of epidemiology, 167 5
M. Hernán (2004)
A definition of causal effect for epidemiological researchJournal of Epidemiology and Community Health, 58
C. Phillips (2003)
Quantifying and Reporting Uncertainty from Systematic ErrorsEpidemiology, 14
S. Greenland, J. Robins, J. Pearl (1999)
Confounding and Collapsibility in Causal InferenceStatistical Science, 14
S. Greenland (2005)
Multiple‐bias modelling for analysis of observational dataJournal of the Royal Statistical Society: Series A (Statistics in Society), 168
A. Whittemore (1978)
Collapsibility of Multidimensional Contingency TablesJournal of the royal statistical society series b-methodological, 40
Francis Cook (1981)
Confounding: essence and detection.American journal of epidemiology, 114 4
J. Schwartz (1998)
Air pollution and hospital admissions for heart disease in eight U.S. counties.Epidemiology, 10 1
J. Hausman (1978)
Specification tests in econometricsApplied Econometrics, 38
M. Hernán, J. Robins (2006)
Estimating causal effects from epidemiological dataJournal of Epidemiology and Community Health, 60
I. Nofroni (1999)
[The statistical test].Professioni infermieristiche, 52 2
D. Hosmer, S. Lemeshow (1991)
Applied Logistic Regression
J. Neuhaus, J. Kalbfleisch, W. Hauck (1991)
A Comparison of Cluster-Specific and Population-Averaged Approaches for Analyzing Correlated Binary DataInternational Statistical Review, 59
J. Cornfield, W. Haenszel, Hammond Ec, A. Lilienfeld, M. Shimkin, Wynder El (1959)
Smoking and lung cancer: recent evidence and a discussion of some questions.Journal of the National Cancer Institute, 22 1
(1988)
WACHOLDER, S.AND LUBIN
T. Lash, R. Silliman (2000)
A Sensitivity Analysis to Separate Bias Due to Confounding from Bias Due to Predicting Misclassification by a Variable That Does BothEpidemiology, 11
J. Spława-Neyman, D. Dabrowska, T. Speed (1990)
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9Statistical Science, 5
J. Pearl, J. Robins (1999)
Causal diagrams for epidemiologic research.Epidemiology, 10 1
Michael Rosenblum (2011)
Marginal Structural Models
Tosiya Sato, Y. Matsuyama (2003)
Marginal Structural Models as a Tool for StandardizationEpidemiology, 14
(2002)
Observational Studies, 2nd edition
G. Ducharme, Y. Lepage (1986)
Testing Collapsibility in Contingency TablesJournal of the royal statistical society series b-methodological, 48
N. Breslow, N. Day, W. Davis (1980)
Statistical methods in cancer research: volume 1- The analysis of case-control studiesIARC scientific publications, 32
When estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z, is to compare estimates of the exposure–outcome association with and without adjustment for Z. This approach is widely believed to be problematic due to the nonlinearity of some exposure-effect measures. When the expected value of the outcome is modeled as a nonlinear function of the exposure, the adjusted and unadjusted exposure effects can differ even in the absence of confounding (Greenland , Robins, and Pearl, 1999); we call this the nonlinearity effect. In this paper, we propose a corrected measure of confounding that does not include the nonlinearity effect. The performances of the simple and corrected estimates of confounding are assessed in simulations and illustrated using a study of risk factors for low birth–weight infants. We conclude that the simple estimate of confounding is adequate or even preferred in settings where the nonlinearity effect is very small. In settings with a sizable nonlinearity effect, the corrected estimate of confounding has improved performance.
Biostatistics – Oxford University Press
Published: Jul 1, 2010
Keywords: Collapsibility; Confounding; Odds ratio
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.