Using Network Meta-Analysis (NMA) in the estimation of Probability of Success (PoS)
Elaona Lemoto, Andrew Bean, PhD
Abstract
Go/no-go decisions punctuate the drug development lifecycle. These decisions are made based on limited and often imperfect data, and often prove incorrect in hindsight: success rates of even Phase-III trials are quite low in many indications (Wong et al, 2019). To support accurate internal decision-making prior to Phase-III, Hampson et al (2021) describe a novel framework for synthesizing information from several sources (available Phase-III data, historical “benchmark” data about success rates of similar clinical programs, the likelihood of safety issues, and other potential risks) into a Bayesian model that predicts Phase-III outcomes, leading to an estimate of the overall probability of success for the program. However, in many indications, the definition of success is inherently relative to a small handful of other compounds in the competitive landscape. This internship project will explore connections between the probability-of-success (POS) framework described by Hampson et al (2021) and network meta-analysis modelling (e.g. , allowing predictions from the framework to feed into to indirect comparisons with specific competitor drugs and further support decision making.
Date
August 26, 2024
Time
8:15 AM – 8:45 AM
Location
Novartis New Hanover Campus, New Jersey, USA