most citedGeneralizing causal inferences from randomized trials: counterfactual and graphical identification

22 citations · 50 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ME20206 cited

Generalized interpretation and identification of separable effects in competing event settings

Mats J. Stensrud, Miguel A. Hernán, Eric J. Tchetgen Tchetgen +3

In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However,…

stat.ME20201 cited

Causal inference with limited resources: proportionally-representative interventions

Aaron L. Sarvet, Kerollos N. Wanis, Jessica Young +3

Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment…

stat.ME201921 cited

Guidelines for estimating causal effects in pragmatic randomized trials

Eleanor J. Murray, Sonja A. Swanson, Miguel A. Hernán

Pragmatic randomized trials are designed to provide evidence for clinical decision-making rather than regulatory approval. Common features of these trials include the inclusion of…

stat.ME201922 cited

Generalizing causal inferences from randomized trials: counterfactual and graphical identification

Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse +1

When engagement with a randomized trial is driven by factors that affect the outcome or when trial engagement directly affects the outcome independent of treatment, the average tre…

stat.ME2019

Study designs for extending causal inferences from a randomized trial to a target population

Issa J. Dahabreh, Sebastien J-P. A. Haneuse, James M. Robins +4

We examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial desig…

stat.ME2019

Towards causally interpretable meta-analysis: transporting inferences from multiple studies to a target population

Issa J. Dahabreh, Lucia C. Petito, Sarah E. Robertson +2

We take steps towards causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target populatio…