معرفی
Nandini Dendukuri, PhD is a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the 5252 de Maisonneuve site. She holds dual affiliations with the Infectious Diseases and Immunity in Global Health Program and the Centre for Outcomes Research and Evaluation (CORE). Academically, she serves as an Associate Professor in the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University, specifically within the Division of Clinical Epidemiology at the MUHC.
Dr. Dendukuri's research program centers on the development of advanced statistical methodologies for diagnostic test accuracy evaluation in the absence of perfect reference standards. Her work spans Bayesian inference, latent class modeling, and meta-analysis techniques specifically designed for diagnostic accuracy studies. She has made seminal contributions to addressing methodological challenges such as conditional dependence between diagnostic tests, verification bias, and limitations of composite reference standards. Her research bridges theoretical statistical methodology with practical applications in infectious disease diagnostics, particularly tuberculosis where perfect reference tests are often unavailable.
An analysis of Dr. Dendukuri's extensive publication record reveals consistent innovation in diagnostic test methodology with increasing sophistication in statistical modeling approaches. Her recent work (2024-2025) demonstrates expansion into diverse application areas including cardiovascular disease, emerging infectious diseases like COVID-19, and novel diagnostic technologies, while maintaining her core methodological focus. Her publications show particular strength in applying latent class models to complex diagnostic scenarios across multiple disease domains.
Dr. Dendukuri actively collaborates with researchers across multiple disciplines, particularly in epidemiology, infectious diseases, and technology assessment. Her work with hospital-based technology assessment units has directly contributed to translating scientific evidence into healthcare policy. She maintains strong connections between statistical theory and practical healthcare applications, frequently developing software implementations (particularly rjags programs) of her methodological innovations to facilitate adoption by the research community.