134 citations · 468 across the 85 of their papers we have counts for
54 papers · 1 filter
Small area learning
Katarzyna Reluga, Nicola Salvati, Setareh Ranjbar +2
Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model. We introduce small area learning (SAL), a learner-agnostic framework…
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
Wenxin Zhang, Rachael Phillips, Mark van der Laan
Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments. However, unlike treatments, their impact…
Longitudinal Adaptive Experimental Design for Learning Multiple Target Estimands with Semiparametric Efficient Inference
Wenxin Zhang, Mark van der Laan
Adaptive designs are increasingly used in clinical trials and digital experiments to improve estimation efficiency by updating treatment randomization probabilities as data accumul…
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…
The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands
Yi Li, Ashkan Ertefaie, Mark van der Laan
For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well und…
Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions
Vanessa Rodriguez, Karla Diaz-Ordaz, Brieuc Lehmann +1
We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We as…
Targeted maximum likelihood estimation for longitudinal two-stage designs with outcome subsampling
Kirsten E. Landsiedel, Maya L. Petersen, Mark J. van der Laan
We consider efficient estimation of causal parameters in longitudinal two-stage designs with outcome subsampling, motivated by resampling designs in HIV-related mortality studies.…
Higher-Order Efficient Estimators: A Review and Simulation-Based Benchmark Study
Zeyi Wang, Mark J. van der Laan
Higher-order efficient estimators extend standard first-order semiparametric estimators by replacing second-order residuals with third- or higher-order terms, potentially enabling…
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…
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…
Targeted Learning Estimation of Sampling Variance for Improved Inference
Yunwen Ji, Mark van der Laan, Alan Hubbard
For robust statistical inference it is crucial to obtain a good estimator of the variance of the proposed estimator of the statistical estimand. A commonly used estimator of the va…
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…
Assessing treatment effects in observational data with missing confounders: A comparative study of practical doubly-robust and traditional missing data methods
Brian D. Williamson, Chloe Krakauer, Eric Johnson +13
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed co…
An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
Wenxin Zhang, Aaron Hudson, Maya Petersen +1
Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In…
Data-Adaptive Identification of Effect Modifiers through Stochastic Shift Interventions and Cross-Validated Targeted Learning
David McCoy, Wenxin Zhang, Alan Hubbard +2
In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. F…
HAL-Based Plug-in Estimation with Pointwise Asymptotic Normality of the Causal Dose-Response Curve
Junming Shi, Wenxin Zhang, Alan E. Hubbard +1
Estimating and obtaining reliable inference for the marginally adjusted causal dose-response curve for continuous treatments without relying on parametric assumptions is a well-kno…
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…
Nonparametric efficient causal estimation of the intervention-specific expected number of recurrent events with continuous-time targeted maximum likelihood and highly adaptive lasso estimation
Helene C. W. Rytgaard, Mark J. van der Laan
Longitudinal settings involving outcome, competing risks and censoring events occurring and recurring in continuous time are common in medical research, but are often analyzed with…
Super Ensemble Learning Using the Highly-Adaptive-Lasso
Zeyi Wang, Wenxin Zhang, Brian S Caffo +2
We introduce the Meta Highly-Adaptive-Lasso Minimum Loss Estimator (M-HAL-MLE), a novel ensemble approach for estimating functional parameters of realistically modeled data distrib…
Targeted Learning on Variable Importance Measure for Heterogeneous Treatment Effect
Haodong Li, Alan E Hubbard, Oliver J Hines +3
Quantifying the heterogeneity of treatment effect is important for understanding how a commercial product or medical treatment affects different population subgroups. While much of…
The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications
Nerissa Nance, Maya L. Petersen, Mark van der Laan +1
The Causal Roadmap outlines a systematic approach to asking and answering questions of cause-and-effect: define the quantity of interest, evaluate needed assumptions, conduct stati…
Unveiling Causal Mediation Pathways in High-Dimensional Mixed Exposures: A Data-Adaptive Target Parameter Strategy
David B. McCoy, Alan E. Hubbard, Mark van der Laan +1
Mediation analysis in causal inference typically concentrates on one binary exposure, using deterministic interventions to split the average treatment effect into direct and indire…