activity
20182024
most citedGeneralizing causal inferences from randomized trials: counterfactual and graphical identification

22 citations · 49 across the 17 of their papers we have counts for

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19 papers · 1 filter

stat.ME2024

Interpretable meta-analysis of model or marker performance

Jon A. Steingrimsson, Lan Wen, Sarah Voter +1

Conventional meta analysis of model performance conducted using datasources from different underlying populations often result in estimates that cannot be interpreted in the contex…

stat.ME2023

Estimating and evaluating counterfactual prediction models

Christopher B. Boyer, Issa J. Dahabreh, Jon A. Steingrimsson

Counterfactual prediction methods are required when a model will be deployed in a setting where treatment policies differ from the setting where the model was developed, or when a…

stat.ME2023

Sensitivity analysis for studies transporting prediction models

Jon A. Steingrimsson, Sarah E. Robertson, Issa J. Dahabreh

We consider the estimation of measures of model performance in a target population when covariate and outcome data are available on a sample from some source population and covaria…

stat.ME202210 cited

Generalizing and transporting inferences about the effects of treatment assignment subject to non-adherence

Issa J. Dahabreh, Sarah E. Robertson, Miguel A. Hernán

We discuss the identifiability of causal estimands for generalizability and transportability analyses, both under perfect and imperfect adherence to treatment assignment. We consid…

stat.ME2022

Robust Estimation of Loss-Based Measures of Model Performance under Covariate Shift

Samantha Morrison, Constantine Gatsonis, Issa J. Dahabreh +2

We present methods for estimating loss-based measures of the performance of a prediction model in a target population that differs from the source population in which the model was…

stat.ME20221 cited

Selection on treatment in the target population of generalizabillity and transportability analyses

Yu-Han Chiu, Issa J. Dahabreh

Investigators are increasingly using novel methods for extending (generalizing or transporting) causal inferences from a trial to a target population. In many generalizability and…