9 papers
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…
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 reuse enables cost-efficient randomized trials of medical AI models
Michael Nercessian, Wenxin Zhang, Alexander Schubert +4
Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hind…
Constructing Confidence Intervals for Infinite-Dimensional Functional Parameters by Highly Adaptive Lasso
Wenxin Zhang, Junming Shi, Alan Hubbard +1
Estimating the conditional mean function is a central task in statistical learning. In this paper, we consider estimation and inference for a nonparametric class of real-valued cad…
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…