Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial.

By Stephen R. Cole, Elizabeth A. Stuart. 2010

522 citations recorded by the source corpus.

Subfields: Emergency Medicine, Statistics and Probability

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Citations in the corpus

Generalizing causal inferences from randomized trials: counterfactual and graphical identification (2019, background): “Yet, this condition (or close variants) has been invoked in prior work [2–8, 45, 46].”
Generalized Adjustment Under Confounding and Selection Biases (2018, background): “The later is usually known as the “inverse probability-of-selection weight (IPSW)” (Cole and Stuart 2010), and, in practice, is estimated by assuming some parametric model such as logistic regression.”
The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. (2013, background): “This information is often useful when thinking about both internal and external validity (generalizability) of the study results (1).”
Improving Generalizations From Experiments Using Propensity Score Subclassification (2013, background): “However, units in the sample are either not in the survey or not locatable, so S ⊄ V (for a medical study example, see Cole & Stuart, 2010).”
Transportability across studies: A formal approach (2011, background): “Fleming and DeMets (1996) leave the impression that correlation fails to serve surrogacy only when it is weak or moderate, while Baker and Kramer (2003) conclude that correlation fails because it is hard, in practice, to accurately estimate slopes and intercepts.”
Causal machine learning for predicting treatment outcomes (2024, unknown): “Likewise, patient populations in RCTs are often not representative of the broader population 93 , but one can account for this through causal ML 94 to better understand the post-approval efficacy of treatments.”
Mechanism Experiments and Policy Evaluations (2011, methodology): “…approach we forecast a policy’s effects in some setting using previous tests of the policy in similar settings—that is, we try to match on the policy’s candidate moderators (for example, Hotz, Imbens, and Mortimer, 2005; Cole and Stuart, 2010; Imbens, 2010; Stuart, Cole, Bradshaw, and Leaf, 2011).”
Worth Weighting? How to Think About and Use Weights in Survey Experiments (2017, background): “These propensities of inclusion are usually estimated by borrowing from the propensity score literature for observational studies (Cole and Stuart 2010).”
From Local to Global: External Validity in a Fertility Natural Experiment (2015, unknown): “Hotz, Imbens, and Mortimer (2005), Cole and Stuart (2010), Stuart et al. (2011), Hartman et al. (2015), and Kern et al. (2016) apply various approaches to extrapolation from one site to another, including matching, inverse probability weighting, and parametric and nonparametric regression…”
A Review of Generalizability and Transportability (2021, methodology): “Inverse probability of participation weighting (IPPW), a Horvitz-Thompsonlike approach (Horvitz and Thompson, 1952), is the most common weighting technique for generalizability (Flores and Mitnik, 2013; Baker et al., 2013; Lesko et al., 2017; Westreich et al., 2017; Correa, Tian and Bareinboim, 2018”
Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign (2011, methodology): “More research is needed for identifying the situations when such an assumption can bemade (see Cole and Stuart 2010; Hartman, Grieve, and Sekhon 2010; Green and Kern 2010a).”
Transportability of Trial Results Using Inverse Odds of Sampling Weights. (2017, methodology): “We would use these odds to calculate a weighted pseudopopulation of 1,600 persons ((1,000 × 1,600/1,000) = 1,600) for Z = 1 and 400 persons ((1,000 × 400/1,000) = 400) Previously, Cole and Stuart (4) introduced inverse probability weights for quantitative generalization of trial results, but they di”
Causal inference and the data-fusion problem (2016, methodology): “Previous analyses (11, 41, 42) have invariably relied on an assumption called “S-ignorability,” i.”