Katherine M. Prioli, MS

Analyst, R enthusiast, nerd.

Recent Publications

More Publications

. Tailored Activity Program (TAP) Costs: Opportunities to Streamline Delivery and Pursue Reimbursement. GSA symposium presentation, 2019.

Project

. Impact of Behavioral Interventions for Chronic Diseases on Health Utility: Assessment of Three Trials of Older Adults. ISPOR poster, 2019.

PDF

. Intervention Delivery Costs and Caregivers' Willingness to Pay for COPE-CT: Interim Findings. GSA symposium presentation, 2018.

Project

Recent & Upcoming Events

More Events

ISPOR 2020
May 16, 2020 12:00 AM
rstudio::conf(2020L)
Jan 27, 2020 8:00 AM
ISPOR 2019
May 19, 2019 12:00 AM

Projects

Selected areas of research focus

Blood Cost Studies

Blood product process frameworks and cost studies.

Dementia Cost Studies

Dementia behavioral support intervention cost studies.

Ocular Cost Studies

Cost studies related to the burden of eye disease.

Vaccination Education Studies

Studies related to vaccine education programs.

Teaching

Recent courses taught

Fall 2019

  • 30:725:341: Economic Modeling of Pharmaceuticals and Other Health Interventions
    • Graduate course at the Rutgers University Ernest Mario School of Pharmacy.
    • Covers concepts in HEOR modeling, including budget impact analysis, decision analytic modeling, and univariate and probabilistic sensitivity analyses.

Coursework

Courses recently completed

Spring 2019

  • MAT 8444: Time Series & Forecasting
    • Final project: Disposable Income, Debt, and Savings: Q1 1980 through Q3 2018
    • Objective of this project was to analyze US household real per-capita disposable income, debt services payments, and personal savings over time, to understand trends in personal disposable income, and to determine whether savings can accurately be predicted from debt.

Fall 2019

  • CSC 8515: Machine Learning
    • Final project: Understanding Correlates of Obesity: Supervised and Unsupervised Machine Learning Approaches
    • Objective of this project was to apply supervised and unsupervised machine learning approaches in R and Python to understand demographic and behavioral characteristics associated with obesity using the 2015-2016 National Health and Nutrition Examination Survey (NHANES) dataset.
    • Supervised approach employed a random forest using n-by-k-fold crossvalidation to classify cases into weight category by Body Mass Index (BMI).
    • Unsupervised approach applied agglomerative and divisive hierarchical clustering methods to unlabeled data, plotting the corresponding dendrograms along with BMI category labels, assessing cluster homogeneity, and comparing performance for the two clustering methods.