5 citations · 11 across the 5 of their papers we have counts for
5 papers
Debiased machine learning for counterfactual survival functionals based on left-truncated right-censored data
Eric R. Morenz, Charles J. Wolock, Marco Carone
Learning causal effects of a binary exposure on time-to-event endpoints can be challenging because survival times may be partially observed due to censoring and systematically bias…
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome
Charles J. Wolock, Susan Jacob, Julia C. Bennett +9
For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and queryin…
SimEngine: A Modular Framework for Statistical Simulations in R
Avi Kenny, Charles J. Wolock
This article describes SimEngine, an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing en…
Assessing variable importance in survival analysis using machine learning
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction t…
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…