3 citations · 10 across the 10 of their papers we have counts for
12 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…
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…
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…
What is Fair? Defining Fairness in Machine Learning for Health
Jianhui Gao, Benson Chou, Zachary R. McCaw +4
Ensuring that machine learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of e…
Exact Inference for Random Effects Meta-Analyses with Small, Sparse Data
Jessica Gronsbell, Zachary R McCaw, Timothy Regis +1
Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many h…
ssROC: Semi-Supervised ROC Analysis for Reliable and Streamlined Evaluation of Phenotyping Algorithms
Jianhui Gao, Clara-Lea Bonzel, Chuan Hong +3
High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medi…
Harnessing electronic health records for real-world evidence
Jue Hou, Rachel Zhao, Jessica Gronsbell +13
While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world…
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
Jessica Gronsbell, Molei Liu, Lu Tian +1
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised…
Semi-Supervised Approaches to Efficient Evaluation of Model Prediction Performance
Jessica Gronsbell, Tianxi Cai
In many modern machine learning applications, the outcome is expensive or time-consuming to collect while the predictor information is easy to obtain. Semi-supervised learning (SSL…