most citedEfficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

stat.ME2026

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…

stat.ME2026

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…

stat.ME2026

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…

stat.ME20261 cited

Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems

Sky Qiu, Susan Gruber, Pamela A. Shaw +2

In a typical two-phase design, a random sample is drawn from the target population in phase 1, during which only a subset of variables is collected. In phase 2, a subsample of the…

stat.ME2025

Regularized Targeted Maximum Likelihood Estimation in Highly Adaptive Lasso Implied Working Models

Yi Li, Sky Qiu, Zeyi Wang +1

We address the challenge of performing Targeted Maximum Likelihood Estimation (TMLE) after an initial Highly Adaptive Lasso (HAL) fit. Existing approaches that utilize the data-ada…

stat.ML2025

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

Toru Shirakawa, Yi Li, Yulun Wu +5

We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies i…