74 citations · 120 across the 18 of their papers we have counts for
13 papers · 1 filter
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 framework for leveraging machine learning tools to estimate personalized survival curves
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of sci…
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
Nina Galanter, Marco Carone, Ronald C. Kessler +1
Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that…
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,…
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…
Assessment of Immune Correlates of Protection via Controlled Vaccine Efficacy and Controlled Risk
Peter B. Gilbert, Youyi Fong, Marco Carone
Immune correlates of protection (CoPs) are immunologic biomarkers accepted as a surrogate for an infectious disease clinical endpoint and thus can be used for traditional or provis…
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.…
Inference on function-valued parameters using a restricted score test
Aaron Hudson, Marco Carone, Ali Shojaie
It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimand…
A general framework for inference on algorithm-agnostic variable importance
Brian D. Williamson, Peter B. Gilbert, Noah R. Simon +1
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response -- in other words, to gaug…
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…
Combining Biomarkers by Maximizing the True Positive Rate for a Fixed False Positive Rate
Allison Meisner, Marco Carone, Margaret S. Pepe +1
Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the tru…
Causal isotonic regression
Ted Westling, Peter Gilbert, Marco Carone
In observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose-response curve…
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…