Generalizing Study Results

2017/03/25 by Catherine R. Lesko, Ashley Buchanan, Ashley L. Buchanan +4 · 6 citations
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Health Systems, Economic Evaluations, Quality of Life #Statistical Methods and Bayesian Inference

paper · doi:10.1097/ede.0000000000000664

openalex publication_date 2017/03/25 · openalex created_date 2017/04/07 · openalex updated_date 2026/08/01

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.

Citations

• The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
• External Validity: From Do-Calculus to Transportability Across Populations
• On causal inference in the presence of interference
• A Theorem on the Manipulability of Redundant Serial Kinematic Chains
• Using causal diagrams to guide analysis in missing data problems
• Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 Trial
• Sample Selection Bias as a Specification Error
• Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
• Causal Diagrams for Epidemiologic Research

Cited by

• A New Targeted-Federated Learning Framework for Estimating Heterogeneity of Treatment Effects: A Robust Framework with Applications in Aging Cohorts
• Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts
• The Case for Case–Cohort
• Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data
• Toward a Clearer Definition of Selection Bias When Estimating Causal Effects
• An evolved interpretation of Austin Bradford Hill’s causal viewpoints and their influence on epidemiologic methods

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