19 citations · 48 across the 17 of their papers we have counts for
11 papers · 1 filter
Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE
Sky Qiu, Nerissa Nance, Rachael Phillips +3
Subgroup analyses within randomized controlled trials are often underpowered due to limited sample sizes. We address this challenge by leveraging trial participants outside the sub…
Estimation of treatment effects in presence of differential use of post-randomization concomitant medication with time-to-event outcomes
Helene C. W. Rytgaard, Edwin Fong, Jens M. Tarp +3
In placebo-controlled randomized trials, the post-randomization use of concomitant medications may be higher in the placebo arm than in the treatment arm. This may dilute the full…
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…
Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Linying Yang, Jens Magelund Tarp, Robin J. Evans +1
Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world…
Exact Simulation of Longitudinal Data from Marginal Structural Models
Xi Lin, Daniel de Vassimon Manela, Chase Mathis +2
Simulating longitudinal data from specified marginal structural models is a crucial but challenging task for evaluating causal inference methods and informing study design. While d…
An Estimator-Robust Design for Augmenting Randomized Controlled Trials with External Real-World Data
Sky Qiu, Jens Tarp, Andrew Mertens +1
Augmenting randomized controlled trials (RCTs) with external real-world data (RWD) has the potential to improve the finite sample efficiency of treatment effect estimators. We desc…
Data fusion for efficiency gain in ATE estimation: A practical review with simulations
Xi Lin, Jens Magelund Tarp, Robin J. Evans
The integration of real-world data (RWD) and randomized controlled trials (RCT) is increasingly important for advancing causal inference in scientific research. This combination ho…
Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processes
Evangelos Dimitriou, Edwin Fong, Jens Magelund Tarp +2
Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal v…
Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
Mark van der Laan, Sky Qiu, Jens Magelund Tarp +1
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and external real-world data (RWD) are available. We decompos…
Combining experimental and observational data through a power likelihood
Xi Lin, Jens Magelund Tarp, Robin J. Evans
Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too…
Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies
Lauren Eyler Dang, Jens Magelund Tarp, Trine Julie Abrahamsen +4
Augmenting a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. To select and analyze the experiment (RCT alone or combined wi…