In an increasingly complex healthcare landscape, network meta-analysis (NMA) has become a powerful tool for comparing multiple treatments, even when direct head-to-head trials are lacking.
By synthesizing direct and indirect evidence, NMA provides a robust framework for assessing treatment effectiveness, safety, and cost-effectiveness, making it an essential component of evidence-based decision-making. Payers, health technology assessment (HTA) agencies, and pharmaceutical companies rely on NMA to:
Rank interventions
Support reimbursement decisions
Strengthen regulatory submissions
Learning objectives
This session will provide an in-depth exploration of NMA methodologies, practical applications, and real-world impact. Attendees will engage with case studies and gain hands-on experience using R packages such as netmeta and multinma, equipping them with practical skills to conduct and interpret NMAs effectively.
Join us to discover how NMA enhances comparative effectiveness research, informs healthcare policies, and drives optimized patient outcomes in an evolving treatment landscape.
Speakers:
Ankit Pawha, MS
Ankit Pahwa has 16 years of work experience in statistical modelling, analytics, and programming and has extensive knowledge in the analysis of patient level data as well as aggregate data in SAS and R, including meta-analysis, survival analysis, cost regression, and HCRU. At ICON, he supports a variety of projects such as literature reviews and network meta-analysis, database studies, chart review studies, cross-sectional surveys, and external/synthetic control arm studies in various disease areas including oncology, autoimmune diseases, respiratory, neurology, pain management and nutrition. Ankit received his Master of Science from the University of Massachusetts Dartmouth.
Daniel Gallardo, PhD, MSc
Daniel Gallardo brings deep expertise in Health Technology Assessment (HTA) and meta-analytic methods, with a strong specialization in Bayesian statistical modelling. His current work focuses on advanced evidence synthesis—including network meta-analysis (NMA), dose-response, and multilevel models—supporting decision-making in healthcare. Proficient in both Bayesian and frequentist approaches, Daniel designs and implements rigorous statistical analysis plans, primarily using R. He has extensive experience with packages such as multinma, MBNMAdose, MBNMAtime, and BUGSnet, ensuring robust and interpretable results for complex HTA submissions.
Nathan Green, PhD
Nathan Green has many years of experience working on a wide range of projects across government and academia in defence and health. Nathan studied mathematics and statistics at the University of Newcastle-Upon-Tyne and obtained a PhD in applied probability from the University of Liverpool. He has recently worked in oncology, tuberculosis, healthcare-acquired infections and sexually transmitted infections. Nathan’s research interests include health economics, survival analysis, evidence synthesis and epidemiology, and he is a keen R programmer.