19 citations · 55 across the 21 of their papers we have counts for
25 papers
Transportability methods for failure-time outcomes under assumptions for relative effect measures
Sarah E Robertson, Lan Wen, Lawson Ung +2
In certain clinical areas, relative effect measures are believed to remain more constant across populations than absolute measures. This suggests that exchangeability assumptions o…
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…
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…
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…
Generalizability analyses with a partially nested trial design: the Necrotizing Enterocolitis Surgery Trial
Sarah E. Robertson, Matthew A. Rysavy, Martin L. Blakely +2
We discuss generalizability analyses under a partially nested trial design, where part of the trial is nested within a cohort of trial-eligible individuals, while the rest of the t…
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…
Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies
Jiabei Yang, Ann W. Mwangi, Rami Kantor +5
The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment eff…
Global sensitivity analysis for studies extending inferences from a randomized trial to a target population
Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse +3
When individuals participating in a randomized trial differ with respect to the distribution of effect modifiers compared compared with the target population where the trial result…
Systematically Missing Data in Causally Interpretable Meta-Analysis
Jon A. Steingrimsson, David H. Barker, Ruofan Bie +1
Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experiment…
Cluster randomized trials designed to support generalizable inferences
Sarah E. Robertson, Jon A. Steingrimsson, Issa J. Dahabreh
Background: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conduct…
Randomized trials and their observational emulations: a framework for benchmarking and joint analysis
Issa J. Dahabreh, Jon A. Steingrimsson, James M. Robins +1
A randomized trial and an analysis of observational data designed to emulate the trial sample observations separately, but have the same eligibility criteria, collect information o…
Extending inferences from a cluster randomized trial to a target population
Issa J. Dahabreh, Sarah E. Robertson, Jon A. Steingrimsson +2
We describe methods that extend (generalize or transport) causal inferences from cluster randomized trials to a target population of clusters, under a general nonparametric model t…
Learning about treatment effects in a new target population under transportability assumptions for relative effect measures
Issa J. Dahabreh, Sarah E. Robertson, Jon A. Steingrimsson
Epidemiologists and applied statisticians often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are ``transportable'' across p…
Sample size calculations for n-of-1 trials
Jiabei Yang, Jon A. Steingrimsson, Christopher H. Schmid
N-of-1 trials, single participant trials in which multiple treatments are sequentially randomized over the study period, can give direct estimates of individual-specific treatment…
Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population
Sarah E Robertson, Jon A Steingrimsson, Issa J Dahabreh
Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is suffici…
Estimating subgroup effects in generalizability and transportability analyses
Sarah E. Robertson, Jon A. Steingrimsson, Nina R. Joyce +2
Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is suffici…
Center-specific causal inference with multicenter trials: reinterpreting trial evidence in the context of each participating center
Sarah E. Robertson, Jon A. Steingrimsson, Nina R. Joyce +2
In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average over centers may not apply t…
Transporting a prediction model for use in a new target population
Jon A. Steingrimsson, Constantine Gatsonis, Issa J. Dahabreh
We consider methods for transporting a prediction model and assessing its performance for use in a new target population, when outcome and covariate information for model developme…
Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data
Jiabei Yang, Issa J. Dahabreh, Jon A. Steingrimsson
We propose Causal Interaction Trees for identifying subgroups of participants that have enhanced treatment effects using observational data. We extend the Classification and Regres…
Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population
Issa J. Dahabreh, Sarah E. Robertson, Lucia C. Petito +2
We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to estimate potential (counterfactual) outcome means and averag…
Deep Learning for Survival Outcomes
Jon Arni Steingrimsson
This manuscripts develops a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the a…
Generalizing trial findings using nested trial designs with sub-sampling of non-randomized individuals
Issa J. Dahabreh, Miguel A. Hernan, Sarah E. Robertson +2
To generalize inferences from a randomized trial to the target population of all trial-eligible individuals, investigators can use nested trial designs, where the randomized indivi…
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…
Subgroup Identification using Covariate Adjusted Interaction Trees
Jon Arni Steingrimsson, Jiabei Yang
We consider the problem of identifying sub-groups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition…