19 citations · 55 across the 21 of their papers we have counts for
4 papers · 1 filter
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…