1 citations · 1 across the 4 of their papers we have counts for
8 papers
Pseudo-value Based Mean Cumulative Count Regression
Zachary R. McCaw, Alex Ocampo, Enrico Giudice +2
The mean cumulative function (MCF) summarizes how events accumulate over time for a recurrent or multi-component endpoint. The MCF, and its integral over a given time horizon, the…
Reliable fairness auditing with semi-supervised inference
Jianhui Gao, Jessica Gronsbell
Machine learning (ML) models often exhibit bias that can exacerbate inequities in biomedical applications. Fairness auditing, the process of evaluating a model's performance across…
Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events
Jessica Gronsbell, Zachary R. McCaw, Isabelle-Emmanuella Nogues +4
As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can b…
Another look at statistical inference with machine learning-imputed data
Jessica Gronsbell, Jianhui Gao, Zachary R. McCaw +2
From structural biology to epidemiology, predictions from machine learning (ML) models increasingly complement costly gold-standard data, enabling faster, more affordable, and scal…
A Common Pipeline for Harmonizing Electronic Health Record Data for Translational Research
Jessica Gronsbell, Vidul Ayakulangara Panickan, Doudou Zhou +11
Despite the growing availability of Electronic Health Record (EHR) data, researchers often face substantial barriers in effectively using these data for translational research due…
Fairmetrics: An R package for group fairness evaluation
Benjamin Smith, Jianhui Gao, Jessica Gronsbell
Fairness is a growing area of machine learning (ML) that focuses on ensuring models do not produce systematically biased outcomes for specific groups, particularly those defined by…