My paper, Predictive-Adjusted Indirect Comparison: A Novel Method for Population Adjustment with Limited Access to Patient-Level Data, co-authored with my PhD supervisors Gianluca Baio and Anna Heath, is up on arXiv. In this article, we present a novel regression adjustment-based method, predictive-adjusted indirect comparison (PAIC), for population adjustment in indirect treatment comparisons. Population-adjusted indirect treatment comparisons such as matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC) are increasingly used to compare treatments in health technology assessments. Such methods estimate treatment effects when there are differences in effect modifiers across studies and when access to patient-level data is limited.
My paper, Methods for Population Adjustment with Limited Access to Individual Patient Data: A Review and Simulation Study, co-authored with my PhD supervisors Gianluca Baio and Anna Heath, is up on arXiv after undergoing the first round of peer-review. Population adjustment methods are increasingly used in health technology assessments when access to patient-level data is limited and there are cross-trial differences in effect modifiers. Popular methods are matching-adjusted indirect comparison (MAIC), based on propensity score weighting, and simulated treatment comparison (STC), a regression adjustment method. We evaluate these methods and the standard Bucher method in a comprehensive simulation study.