# Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE

arXiv: https://arxiv.org/abs/2605.15483 · HTML: https://mlsanity.com/paper/2605.15483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 16 of 16)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score](https://mlsanity.com/paper/2012.09935/) (2020)
- [Data Integration in Causal Inference](https://mlsanity.com/paper/2110.01106/) (2021)
- [sparsegl: An R Package for Estimating Sparse Group Lasso](https://mlsanity.com/paper/2208.02942/) (2022)
- [Adaptive debiased machine learning using data-driven model selection techniques](https://mlsanity.com/paper/2307.12544/) (2023)
- [Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data](https://mlsanity.com/paper/2405.07186/) (2024)
- [Lassoed Tree Boosting](https://mlsanity.com/paper/2205.10697/) (2022)
- [Data fusion for efficiency gain in ATE estimation: A practical review with simulations](https://mlsanity.com/paper/2407.01186/) (2024)
- [Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations](https://mlsanity.com/paper/2604.20059/) (2026)
- [Using shrinkage methods to estimate treatment effects in overlapping subgroups in randomized clinical trials with a time-to-event endpoint](https://mlsanity.com/paper/2407.11729/) (2024)
- [An Estimator-Robust Design for Augmenting Randomized Controlled Trials with External Real-World Data](https://mlsanity.com/paper/2501.17835/) (2025)
- ["Within-trial" prognostic score adjustment is targeted maximum likelihood estimation](https://mlsanity.com/paper/2507.23446/) (2025)
- [Highly Adaptive Principal Component Regression](https://mlsanity.com/paper/2602.10613/) (2026)
- [Unified implementation and comparison of Bayesian shrinkage methods for treatment effect estimation in subgroups](https://mlsanity.com/paper/2603.21967/) (2026)

## Abstract (arXiv)

Subgroup analyses within randomized controlled trials are often underpowered due to limited sample sizes. We address this challenge by leveraging trial participants outside the subgroup of interest to augment estimation within the subgroup. Specifically, we study two Targeted Maximum Likelihood Estimators (TMLEs) that borrow information from non-subgroup participants within the same trial: a TMLE with pooled regression (TMLE-PR) and an Adaptive Targeted Maximum Likelihood Estimator (A-TMLE). Both estimators enable information sharing without relying on any external real-world data, thereby capitalizing on key strengths of the trial: most importantly, the protection against bias afforded by the randomized treatment, but also harmonized data collection, and consistent treatment and outcome definitions. The general strategy proposed here directly advances the priorities of key regulatory agencies, including the FDA, by improving the precision of subgroup-specific treatment effect estimates without introducing external sources of bias, thereby facilitating rigorous inference to support equitable labeling, access, and post-market evaluation. In a case study based on analysis of data from a cardiovascular outcome trial (LEADER, NCT01179048), we estimate the risk reduction of major adverse cardiac events (MACE) under liraglutide treatment among Black and Asian subgroups -- each comprising less than 10% of the trial population -- using the proposed estimators that borrow information from the remainder of the trial. Using A-TMLE, in particular, we find estimated absolute MACE risk reductions of 1.6, 1.5, and 1.5 percentage points among Asian participants and 2.1, 2.0, and 2.1 percentage points among Black participants at 365, 540, and 730 days, respectively, with 95% confidence intervals excluding the null at each time point.