Andrea (A) Gabrio

Birthdate: 21-11-1990


Personal statement:

I am an assistant professor in Statistics in the Department of methodology and statistics of the Faculty of Health Medicine and Life Sciences (FHML) at Maastricht University (NL).

My main interests are in Bayesian statistical modelling for cost-effectiveness analysis and decision-making problems in the health systems. During my PhD I have specifically focused on the study and adoption of Bayesian methods to handle missing data in health economic evaluations and to assess the impact of their uncertainty on the output of the decision-making process. My research area involves different topics: from systematic literature reviews, case study applications, survival analysis, meta-analytic methods, multilevel models and trial-based clinical and economic analyses. You can find more information about me on my personal webpage, where you can also find my CV with more details on what I have done so far and my current research interests.

I have collaborated with the Statistics for Health Economic Evaluation research group in the Department of Statistical Science at UCL, which is mainly focused on the development and application of Bayesian methods for health economic evaluations. I have also collaborated with the Health Economics Analysis and Research methodology Team in the Institute for Clinical Trials and Methodology at UCL.


I first received my BSc in Applied Economics at the University of Pavia (Italy) in 2012 and then graduated from my MSc in Statistics and Econometrics at the Univeristy of Essex (UK) in 2015. In 2019 I completed a PhD programme in Statistics at University College London, during which I also visited for a short period the Department of Statistics at University of Florida (USA). After completing my PhD, I worked as a research fellow at UCL until 2020 where I closely collaborated with people from both private industry and academia on different reaseach projects.

Missing data; Multiple imputation; Longitudinal data; Multilevel models

I am very interested in the analysis of longitudinal data, with a focus on different types of statistical methods to deal with missingness. My preferred statistical programming software, and the one I am most familiar with, is R/RStudio by far, which I used to write the package missingHE, which is specifically aimed at handling missing data in trial-based economic evaluations using Bayesian methods. You can also find the most updated version of the package on my GitHub page. In addition, I do possess a good knowledge of other software such as STATA and MATLAB, and I am quite expert in the use of free open-source Bayesian software programs, such as OpenBUGSJAGS and STAN.



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