Alessandro Gasparini, PhD

Principal Statistical Methodologist

Alessandro Gasparini is a biostatistician and software developer with substantial experience in working with real-world evidence studies and a strong background in survival analysis, longitudinal data analysis, multilevel modelling, and computational statistics, including methods development and implementation. He is passionate about building tools to enable more efficient and reproducible data analysis pipelines and to bring modern statistical methods to practice. To that end, he has developed R packages that have been downloaded thousands of times from the Comprehensive R Archive Network (CRAN) and he is a co-chair of the openstatsware working group, a cross-industry scientific working group recognised by the American Statistical Association (ASA) Biopharmaceutical section (BIOP) and by the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI). He is also an affiliated researcher at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet and an Associate Editor of the journal Biostatistics. Alessandro joined Red Door Analytics in November 2022.

Education

  • PhD in Biostatistics, University of Leicester, UK, awarded in 2020 with a thesis titled Multilevel Modelling of Electronic Health Records

  • MSc in Biostatistics and Experimental Statistics, University of Milano-Bicocca, Italy, 2015

  • BSc in Statistics and Computing Technologies, University of Padua, Italy, 2012

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Videos

State-of-the-art statistical models for modern HTA
At @RedDoorAnalytics, we develop methodology and software for efficient modelling of biomarkers, measured repeatedly over time, jointly with survival outcomes, which are being increasingly used in cancer settings. We have also developed methods and software for general non-Markov multi-state survival analysis, allowing for the development of more plausible natural history models, where patient history can […]
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Videos

Multilevel (hierarchical) survival models: Estimation, prediction, interpretation
Hierarchical time-to-event data is common across various research domains. In the medical field, for instance, patients are often nested within hospitals and regions, while in education, students are nested within schools. In these settings, the outcome is typically measured at the individual level, with covariates recorded at any level of the hierarchy. This hierarchical structure […]
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Statistical Primers

What are competing risks?
Competing risks In survival analysis, competing risks refer to the situation when an individual is at risk of experiencing an event that precludes the event under study to occur. Competing risks commonly occur in studies of cause-specific mortality, as all other causes of death than the one under study might happen before the individuals “have […]
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