Alex Luedtke (he/him)
Associate Professor, University of Washington
aluedtke@uw.edu | |
UW Box Number | 354322 |
Homepage | Personal Home Page |
ORCID iD | 0000-0002-9936-3236 |
Alex Luedtke is an Associate Professor in the Department of Statistics at the University of Washington (UW). He also has an adjunct appointment in the Department of Biostatistics at UW and an affiliate appointment in the Vaccine and Infectious Disease Division at the Fred Hutchinson Cancer Research Center.
His methodological research focuses on developing efficient estimators in problems arising in a variety of areas, including in policy learning and infectious disease studies. He derives such estimators analytically using tools from semiparametric efficiency theory and numerically using minimax optimization schemes. He also serves as a study statistician for the HIV Vaccine Trials Network and the Covid-19 Prevention Network.
Alex is a recipient of an NIH Director’s New Innovator Award, AWS Machine Learning Research Award, Eric Lehmann Citation, and National Defense Science and Engineering Graduate (NDSEG) fellowship.
Preprints
Data fusion using weakly aligned sources
Sijia Li, Peter B. Gilbert, Alex Luedtke
We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make…
Improved Efficiency for Cross-Arm Comparisons via Platform Designs
Tzu-Jung Huang, Alex Luedtke, the AMP Investigators Group
Though platform trials have been touted for their flexibility and streamlined use of trial resources, their statistical efficiency is not well understood. We…
Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data
Xiudi Li, Sijia Li, Alex Luedtke
We present a general framework for using existing data to estimate the efficiency gain from using a covariate-adjusted estimator of a marginal treatment effect…
Inference for treatment-specific survival curves using machine learning
Ted Westling, Alex Luedtke, Peter Gilbert, Marco Carone
In the absence of data from a randomized trial, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a…
Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome
Tzu-Jung Huang, Alex Luedtke, Ian W. McKeague
This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for…
One-Step Estimation of Differentiable Hilbert-Valued Parameters
Alex Luedtke, Incheoul Chung
We present estimators for smooth Hilbert-valued parameters, where smoothness is characterized by a pathwise differentiability condition. When the parameter…
Individualized treatment rules under stochastic treatment cost constraints
Hongxiang Qiu, Marco Carone, Alex Luedtke
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited…
Efficient Estimation Under Data Fusion
Sijia Li, Alex Luedtke
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the…
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
Sijia Li, Xiudi Li, Alex Luedtke
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by…
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
Alex Luedtke, Incheoul Chung, Oleg Sofrygin
We frame the meta-learning of prediction procedures as a search for an optimal strategy in a two-player game. In this game, Nature selects a prior over…
Universal sieve-based strategies for efficient estimation using machine learning tools
Hongxiang Qiu, Alex Luedtke, Marco Carone
Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a…
Efficient Principally Stratified Treatment Effect Estimation in Crossover Studies with Absorbent Binary Endpoints
Alex Luedtke, Jiacheng Wu
Suppose one wishes to estimate the effect of a binary treatment on a binary endpoint conditional on a post-randomization quantity in a counterfactual world in…
Sequential Double Robustness in Right-Censored Longitudinal Models
Alexander R. Luedtke, Oleg Sofrygin, Mark J. van der Laan, Marco Carone
Consider estimating the G-formula for the counterfactual mean outcome under a given treatment regime in a longitudinal study. Bang and Robins provided an…
An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions
Alexander R. Luedtke, Marco Carone, Mark J. van der Laan
We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to…
Partial Bridging of Vaccine Efficacy to New Populations
Alexander R. Luedtke, Peter B. Gilbert
Suppose one has data from one or more completed vaccine efficacy trials and wishes to estimate the efficacy in a new setting. Often logistical or ethical…
Parametric-Rate Inference for One-Sided Differentiable Parameters
Alexander R. Luedtke, Mark J. van der Laan
Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to…
Evaluating the Impact of Treating the Optimal Subgroup
Alexander R. Luedtke, Mark J. van der Laan
Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not…
The Generalized Mean Information Coefficient
Alexander Luedtke, Linh Tran
Reshef & Reshef recently published a paper in which they present a method called the Maximal Information Coefficient (MIC) that can detect all forms of…