181 citations · 241 across the 29 of their papers we have counts for
18 papers · 1 filter
Data fusion using weakly aligned sources
Sijia Li, Peter B. Gilbert, Rui Duan +1
We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned d…
Adaptive debiased machine learning using data-driven model selection techniques
Lars van der Laan, Marco Carone, Alex Luedtke +1
Debiased machine learning estimators for smooth functionals in nonparametric models can exhibit substantial variability and instability, often leading practitioners to instead rely…
A general adaptive framework for multivariate point null testing
Adam Elder, Marco Carone, Peter Gilbert +1
As a common step in refining their scientific inquiry, investigators are often interested in performing some screening of a collection of given statistical hypotheses. For example,…
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 fill this gap by estab…
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 attention in existing…
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 right-censored outcome d…
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 estimation of a variety of par…
Improved inference for vaccine-induced immune responses via shape-constrained methods
Nilanjana Laha, Zoe Moodie, Ying Huang +1
We study the performance of shape-constrained methods for evaluating immune response profiles from early-phase vaccine trials. The motivating problem for this work involves quantif…
Inference for treatment-specific survival curves using machine learning
Ted Westling, Alex Luedtke, Peter Gilbert +1
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 time-to-event outcome.…
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 in a future randomiz…
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior Knowledge
Hongxiang Qiu, Alex Luedtke
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too…
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 nonparametric model. One ap…
Selecting optimal subgroups for treatment using many covariates
Tyler J. VanderWeele, Alex R. Luedtke, Mark J. van der Laan +1
We consider the problem of selecting the optimal subgroup to treat when data on covariates is available from a randomized trial or observational study. We distinguish between four…
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 which all subjects re…
Sequential Double Robustness in Right-Censored Longitudinal Models
Alexander R. Luedtke, Oleg Sofrygin, Mark J. van der Laan +1
Consider estimating the G-formula for the counterfactual mean outcome under a given treatment regime in a longitudinal study. Bang and Robins provided an estimator for this quantit…
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 considerations make runnin…
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 estimate the maximal p…
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 there exists some subset…