Arman is a Biostatistics PhD candidate in the Department of Biostatistics, Epidemiology, and Informatics whose work focuses on developing Bayesian nonparametric and machine learning methods for analyzing cost outcomes. Cost outcomes are highly pathological – they are often censored, zero-inflated, multimodal, skewed, etc. In these settings, the rigid assumptions underpinning traditional regression models are often violated. Accurate comparisons of treatments in terms of costs and cost-effectiveness necessitate flexible modeling with minimal assumptions. To that end, Arman has worked on developing novel methodology for accomplishing key tasks of interest to health economists and researchers – e.g. cost prediction, causal estimation, clustering analyses, and cost-effectiveness analysis.
Arman holds and MS in Biostatistics from Penn and a BA in Quantitative Economics from Providence College. Before Penn, he was a Senior Analyst in the Health Economics and Outcomes Research (HEOR) team at Analysis Group, a Boston-based consultancy providing statistical analysis support to leading pharmaceutical companies.