Ongoing Research Priorities
Surveillance Methodology and Data Analysis
My research in surveillance methodology and data analysis focuses on optimizing informatics pipelines to extract and harmonize medical records for robust injury surveillance. I employ epidemiological techniques to quantify and adjust for inherent information biases in observational data, and leverage Bayesian approaches to enhance the precision and analytical rigor of surveillance efforts. My work in this area is intended to drive actionable insights for evidence-based injury prevention.
Long-term effects of injury
My research on the long-term effects of injury in aging populations centers on understanding how cumulative injury experiences impact health later in life. I particularly focus on aging women as a target population, and aim to address measurement challenges associated with the retrospective assessment of life course injury histories. I employ advanced epidemiological techniques to address recall bias and misclassification issues, and aim to generate insights that inform targeted interventions in aging women.
Project-related source code
I have shared the Stan program used in my recent publication on Bayesian modeling of sports injury incidence rates. The model applies a negative binomial regression with cluster-level random effects, offset by athlete-exposures, and is designed for use in injury surveillance and epidemiology research. The GitHub repository includes the full Stan code, documentation of the required data structure, licensing information, and citation details.
Chandran, A., Lambert, B. Bayesian methods for estimating injury rates in sport injury epidemiology. Inj. Epidemiol. 12, 31 (2025). https://doi.org/10.1186/s40621-025-00583-z
Accompanying Stan program, documentation, and licensing information are available on GitHub: https://github.com/avinashsatishchandran/Surveillance_inj_rate
In the spirit of open science, I try to share relevant, project-specific source code developed through my work in injury surveillance and epidemiological methods. These repositories include Stan programs, data processing pipelines, and analytic tools that support model development, reproducibility, and transparent dissemination of methods. Making these resources available is intended to facilitate collaboration, reuse, and extension by the broader research community, while advancing open and rigorous approaches to injury epidemiology.
Stan implementation of a multivariable negative binomial model for injury surveillance
© Avinash Chandran, 2025.