Preeti Chachlani

I’m a Lead Biostatistician focused on turning large-scale healthcare data into reliable, decision-ready insights. My work centers on causal inference and rigorous statistical modeling for real-world public health questions.

At Brown University School of Public Health, I research the effects of vaccines (including COVID and Shingles) among Medicare populations, analyzing datasets with over 38M observations. I develop complex SAS code (including parallel processing) and evaluate clinical questions like drug cascades and polypharmacy using causal inference methods such as clone-censor-weighting.

I also support biostatistics at Massachusetts General Hospital, assisting with grant applications and feasibility assessments for studies such as cystectomy and pancreatic cancers. I curate CMS data using advanced SAS procedures to ensure analyses are structured and reproducible for research teams.

Earlier roles strengthened my end-to-end data and modeling workflow, from cleaning and de-identifying Medicaid claims and enrollment data with advanced SAS to leading version control with Git and building documentation/SOPs. I bring additional experience from policy-focused analytics and from prior risk modeling work at State Bank of India, and I’m recognized for research posters and SAS specialization.