Xiaoxiao Ling

I am a postdoc researcher in health economics based at Nuffield Department of Primary Care Health Sciences, University of Oxford. My current work focusses on trial-based economic evaluations and observational data analysis for orthopaedic interventions and sleep disorders.

Broadly, my research interest lies in health economic evaluations and their statistical methodologies, with a particular focus on handling missing data and applying Bayesian methods in health economics.

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.

I look forward to collaborating with researchers who share an interest in health economics and its statistical methodology.

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. PhamarcoEconomics. (Accepted)

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.

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.