most citedGeneralizing and transporting causal inferences from randomized trials in the presence of trial engagement effects

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ME2026

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.…

stat.ME2026

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…

stat.ME2024

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…

stat.ME20242 cited

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…

stat.ME2024

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…