Sarkar, R., & Sun, J. (2024). Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data. Mathematics, 12(17), 2705.
Sarkar, R., Manage, S., & Gao, X. (2023). Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations. Annals of Data Science, 1-26.
Sarkar, R.; Sun, J. (October, 2024). Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data. RMSC 2024.
Sarkar, R.; Sun, J. (October, 2024). Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data. AISC 2024.
Sarkar, R.; Sun, J. (April, 2023). Data Mining for Safety Signal Detection in Clinical Trials. International Mathematics and Statistics Student Research Symposium (IMSSRS) 2023.
Sarkar, R. (March, 2023). Quantitative Methods for Safety Signal Detection in Clinical Trials. Empowering Women of Mathematics (EWM) Conference, Wake Forest University, Winston-Salem, NC.
Poster presentation at SAS Intern Expo 2024
Poster presentation at UNCG Showcase 2024
Sarkar, R. (August, 2024). Double Programming and Unit Testing for Validation of JMP Clinical Safety Analysis Reports pertaining to Events, Findings, and Interventions. Poster session presented at SAS Intern Expo 2024, Cary, NC.
Sarkar, R.; Sun, J. (April, 2024). Safety Signal Detection for Post-marketing Clinical Data. Poster session presented at UNCG Showcase 2024, Greensboro, NC.
Sarkar, R., Manage, S., Gao, X. (May, 2022). Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations. Poster session presented virtually at NISS Graduate Student Research Conference 2022.
This is the accompanying R package with the feature selection method proposed in Sarkar, R., Manage, S., & Gao, X. (2023). Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations. Annals of Data Science, 1-26.
All the code notebooks and assignments that were completed as part of the AI in Medicine Specialization course through Coursera in July 2023.
This repository consists of the code notebooks prepared for individual tasks, in addition to some report samples for the COVID-19 Data Analysis Project, completed in the Spring 2022 semester.
Double Programming of JMP Clinical Safety Reports @ JMP Statistical Discovery, SAS
Developed an interactive Shiny application designed for use in pharmacometric modeling for the development of onco-therapeutic drugs.
Interactive Shiny App for Dosimetry @ Novartis Pharmaceuticals
Developed an interactive Shiny application designed for use in pharmacometric modeling for the development of onco-therapeutic drugs.
COVID Data Analysis
Developed an analytical framework tailored for COVID-19 data analysis, aimed at uncovering intricate patterns and associations with key predictors sourced from enrichment datasets, leveraging the robust capabilities of Python 3.0 libraries including Pandas, Numpy, Plotly, among others.
Study of Factors Affecting Coronary Heart Disease
Application of various supervised and unsupervised learning methods to study the association of predictors such as systolic blood pressure, age, etc., with the incidence of coronary heart disease (CHD).
Performance Evaluation of Resampling Methods in Claims Reserving
Implemented three well-known resampling methods — bootstrapping, randomization, and jackknife — to forecast future reserves for claims losses. Compared predictions from each method with actual reserves computed using the Chain-Ladder approach, a prevalent actuarial loss reserving technique.
Heart Failure Data Analysis
Conducted statistical analysis on data pertaining to patients at risk of death due to heart failure, employing generalized linear models.
Residual Diagnostics for Variogram Fitting (Spatial Data Analysis)
Explored residual diagnostics methods for variogram fitting in geological data, alongside investigating an interactive tool for implementing these diagnostics in R (utilizing the vardiag package).
Study of Association between Internet Use and Suicide
Utilized the GapMinder dataset as a key component of an online course in data analysis and interpretation provided by Wesleyan University through Coursera. Documented course advancements through blog entries here.
Department of Mathematics & Statistics
University of North Carolina at Greensboro
116 Petty Building
PO Box 26170
Greensboro, NC 27402-6170
Email: rsarkar@uncg.edu, rsarkar2@icloud.com
LinkedIn: www.linkedin.com/in/reetika-sarkar
“If you are working on something that you really care about, you don’t have to be pushed. The vision pulls you.” — Steve Jobs