The table 2 fallacy: presenting and interpreting confounder and modifier coefficients

It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We...

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Veröffentlicht in:	American journal of epidemiology Jg. 177; H. 4; S. 292
Hauptverfasser:	Westreich, Daniel, Greenland, Sander
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 15.02.2013
Schlagworte:	Confounding Factors (Epidemiology) Epidemiologic Studies HIV Infections - epidemiology HIV Seropositivity - complications HIV Seropositivity - epidemiology Humans Logistic Models Mathematical Computing Multivariate Analysis North Carolina - epidemiology Odds Ratio Regression Analysis Risk Assessment Risk Factors Smoking - adverse effects Statistics as Topic Stroke - epidemiology Stroke - etiology Violence - statistics & numerical data Work - statistics & numerical data North Carolina
ISSN:	1476-6256, 1476-6256
Online-Zugang:	Weitere Angaben
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Abstract	It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.
AbstractList	It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates. It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.
Author	Westreich, Daniel Greenland, Sander
Author_xml	– sequence: 1 givenname: Daniel surname: Westreich fullname: Westreich, Daniel email: daniel.westreich@duke.edu organization: Department of Obstetrics and Gynecology, Duke Global Health Institute, Duke University, DUMC3967, Durham, NC 27710, USA. daniel.westreich@duke.edu – sequence: 2 givenname: Sander surname: Greenland fullname: Greenland, Sander
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/23371353$$D View this record in MEDLINE/PubMed
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Snippet	It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more...
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SubjectTerms	Confounding Factors (Epidemiology) Epidemiologic Studies HIV Infections - epidemiology HIV Seropositivity - complications HIV Seropositivity - epidemiology Humans Logistic Models Mathematical Computing Multivariate Analysis North Carolina - epidemiology Odds Ratio Regression Analysis Risk Assessment Risk Factors Smoking - adverse effects Statistics as Topic Stroke - epidemiology Stroke - etiology Violence - statistics & numerical data Work - statistics & numerical data
Title	The table 2 fallacy: presenting and interpreting confounder and modifier coefficients
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