Generalizing Study Results: A Potential Outcomes Perspective

Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally...

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Vydáno v:	Epidemiology (Cambridge, Mass.) Ročník 28; číslo 4; s. 553
Hlavní autoři:	Lesko, Catherine R, Buchanan, Ashley L, Westreich, Daniel, Edwards, Jessie K, Hudgens, Michael G, Cole, Stephen R
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.07.2017
Témata:	Bias Causality Epidemiologic Methods Female Humans Male Predictive Value of Tests Reproducibility of Results Statistics as Topic United States
ISSN:	1531-5487, 1531-5487
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Abstract	Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example.
AbstractList	Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example. Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example.Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example.
Author	Cole, Stephen R Lesko, Catherine R Buchanan, Ashley L Westreich, Daniel Hudgens, Michael G Edwards, Jessie K
Author_xml	– sequence: 1 givenname: Catherine R surname: Lesko fullname: Lesko, Catherine R organization: From the aDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC; bDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; cDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC; and dDepartment of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA – sequence: 2 givenname: Ashley L surname: Buchanan fullname: Buchanan, Ashley L – sequence: 3 givenname: Daniel surname: Westreich fullname: Westreich, Daniel – sequence: 4 givenname: Jessie K surname: Edwards fullname: Edwards, Jessie K – sequence: 5 givenname: Michael G surname: Hudgens fullname: Hudgens, Michael G – sequence: 6 givenname: Stephen R surname: Cole fullname: Cole, Stephen R
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28346267$$D View this record in MEDLINE/PubMed
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Title	Generalizing Study Results: A Potential Outcomes Perspective
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