Population adjustment with limited individual patient data

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.

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