I am a postdoc researcher in health economics based at Nuffield Department of Primary Care Health Sciences, University of Oxford. I develop and apply statistical methodology for complex economic and longitudinal outcomes in trials and cohort studies, with interests in Bayesian modelling, missing data, measurement error and study design.

Previously, I did my PhD at the Department of Statistical Science, University College London, under the supervision of Prof Gianluca Baio and Dr Andrea Gabrio.

Broadly, my research interest includes:

• Health economic evaluations
• Longitudinal data analysis
• Missing data
• Measurement error
• Bayesian modelling
• Health economics study design

Selected Publications

Ling, X., Gabrio, A., & Baio, G. (2025). Bayesian Cost-Effectiveness Analysis Using Individual-Level Data is Sensitive to the Choice of Uniform Priors on the Standard Deviations for Costs in Log-Normal Models. PharmacoEconomics, 43, 1309–1321.

We found that Bayesian cost-effectiveness analyses are sensitive to the choice of the Uniform prior distributions on log cost standard deviations when costs data are assumed to be log-normally distributed and contain zero values.

Ling, X., Gabrio, A., Mason, A., & Baio, G. (2022). A Scoping Review of Item-Level Missing Data in Within-Trial Cost-Effectiveness Analysis. Value in Health, 25(9), 1654-1662.

We identified 87 trial-based CEAs, and found that complete case analysis or available case analysis (CCA/ACA) and multiple imputation (MI) were the most popular methods to handle missing costs and QoL in base-case analysis. However, CCA/ACA dominated sensitivity analysis. Missing costs were widely imputed at item level via MI, while missing QoL was usually imputed at the more aggregated time point level during the follow-up via MI.