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We demonstrate their use with an illustrative example. We describe two estimators that have been proposed to generalize results from a study sample to a target population when the study sample was not randomly sampled from the target population: a generalization of the g-formula 13 (adjustment formula) 14 and an inverse probability of sampling weighted estimator. A set of identification assumptions sufficient for generalizability are outlined, and parallels are noted with identification assumptions sufficient for internal validity. Here we will mainly discuss generalizability, as in 5, 7, 12.
#TRIALS FUSION GREENHOUSE EFFECT FULL#
Generalizability is concerned with making inference from a possibly biased sample of the target population back to the full target population (including the study sample), while transportability concerns making inference for a target population when the study sample and the target population are partially or completely non-overlapping. External validity can be divided into two problems: generalizability and transportability. 7 The effect in the study sample is sometimes called the sample average treatment effect, while the effect of interest is sometimes called the (target) population average treatment effect. 9– 11 The purpose of this paper is to review recent developments in generalizability, one facet of external validity, using the potential outcomes framework.įor the purposes of this paper, external validity refers to the extent to which an internally valid effect measured in a study sample is an (asymptotically) unbiased estimator of the treatment effect in the population of interest (henceforth, the target population). Although there have been recent advances in methods for drawing externally valid inferences, particularly in statistics and computer science, 2– 6 those concepts have not yet been widely accepted in epidemiology 7, 8 as is evidenced by ongoing debates as to the importance of representativeness in study samples. 1 However, the external validity of effect estimates has received considerably less attention. Great care is generally taken in epidemiologic studies to ensure the internal validity of causal effect estimates. 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.Įpidemiology as a discipline seeks to identify causes of disease for the purpose of intervening to improve public health. We also require correct model specification.
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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. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. 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. 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. Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates however, external validity has received considerably less attention.