164 citations · 180 across the 14 of their papers we have counts for
18 papers
Estimand frameworks for causal inference in clinical research and regulatory decision making
Miguel A. Hernán, Mats J. Stensrud, Xabier García-Albéniz +2
Causal inference for clinical and regulatory decision making requires a precise definition of the causal estimand, that is, what we want to estimate. Three frameworks are commonly…
Comparing randomized trials
Yu-Han Chiu, Issa J. Dahabreh
Disagreements between randomized trials that address the same causal question pose challenges for the interpretation and synthesis of evidence. We sketch a causal framework for com…
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…
Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches
Laura B. Balzer, Lei Nie, Issa J. Dahabreh +15
While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for ba…
Causally-interpretable meta-analysis using aggregate data
Qingyang Shi, Wouter van Amsterdam, Sacha la Bastide-van Gemert +2
Evidence syntheses and meta-analyses are used to inform clinical practice guidelines and health economic evaluations. However, heterogeneity of treatment effects poses a significan…
Using the target trial framework for combining information: external comparator analyses and other applications
Lawson Ung, Miguel A. Hernán, Issa J. Dahabreh
We describe how the target trial framework can be used to plan and report analyses that attempt to answer causal questions by combining information from multiple, diverse sources.…
Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
Lan Wen, Issa J. Dahabreh, Yu-Han Chiu
Randomized controlled trials often enroll participants whose characteristics differ from those of a target population, which can limit the generalizability of the estimated treatme…
Constructing external comparator groups via transportability in mean or in effect measure
Lawson Ung, Guanbo Wang, Sebastien Haneuse +3
Learning about causal effects in target populations and their subsets may be facilitated by combining information from multiple sources. One major class of study designs that combi…
Considerations for the Integration of Randomized Controlled Trials and Real-World Data
Sky Qiu, Charles Barr, Lauren Dang +18
As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observati…
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
Rickard Karlsson, Piersilvio De Bartolomeis, Issa J. Dahabreh +1
Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover individual-level treatment effect heterogeneity, limiti…
Efficient Randomized Experiments Using Foundation Models
Piersilvio De Bartolomeis, Javier Abad, Guanbo Wang +4
Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the ot…
The role of assignment in defining and identifying causal effects in randomized trials
Issa J. Dahabreh, Lawson Ung, Miguel A. Hernán +1
In randomized trials, the per-protocol effect, that is, the effect of being assigned a treatment strategy and receiving treatment according to the assigned strategy, is sometimes t…
Generalizing and transporting causal inferences from randomized trials in the presence of trial engagement effects
Lawson Ung, Tyler J. VanderWeele, Issa J. Dahabreh
Trial engagement effects are effects of trial participation on the outcome that are not mediated by treatment assignment. Most work on extending (generalizing or transporting) caus…
Robust integration of external control data in randomized trials
Rickard Karlsson, Guanbo Wang, Piersilvio De Bartolomeis +2
One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment studied in the trial during rout…
Identification strategies for combining an experimental study with external data
Lawson Ung, Guanbo Wang, Sebastien Haneuse +2
There is increasing interest in combining information from experimental studies, including randomized and single-group trials, with information from external experimental or observ…
Efficient estimation of subgroup treatment effects using multi-source data
Guanbo Wang, Alexander Levis, Jon Steingrimsson +1
Investigators often use multi-source data (e.g., multi-center trials, meta-analyses of randomized trials, pooled analyses of observational cohorts) to learn about the effects of in…
Causal inference under transportability assumptions for conditional relative effect measures
Guanbo Wang, Alexander Levis, Jon Steingrimsson +1
When extending inferences from a randomized trial to a new target population, the transportability condition for conditional difference effect measures is invoked to identify the m…
Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals
Issa Dahabreh, Sarah Robertson, Eric Tchetgen Tchetgen +2
We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline cova…