#79 Decision-Making & Cost Effectiveness Analysis for Health Economics, with Gianluca Baio
Learning Bayesian Statistics
The Biggest Barriers in Bayesian Statistics
Bayesian statistics can be very complicated because you may need to learn new tools. The level of complexity depends on how well and how realistically you're trying to model your problem, your world. And it just so happens, I think that being Bayesian has some advantages because it lets you push the boundaries of how realistic your model would be. It's not something that anybody can just whip up on pen and paper.
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Decision-making and cost effectiveness analyses rarely get as important as in the health systems — where matters of life and death are not a metaphor. Bayesian statistical modeling is extremely helpful in this field, with its ability to quantify uncertainty, include domain knowledge, and incorporate causal reasoning.
Specialized in all these topics, Gianluca Baio was the person to talk to for this episode. He’ll tell us about this kind of models, and how to understand them.
Gianluca is currently the head of the department of Statistical Science at University College London. He studied Statistics and Economics at the University of Florence (Italy), and completed a PhD in Applied Statistics, again at the beautiful University of Florence.
He’s also a very skilled pizzaiolo — so now I have two reasons to come back to visit Tuscany…
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, and Arkady.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Gianluca’s website: https://gianluca.statistica.it/
- Gianluca on GitHub: https://github.com/giabaio
- Gianluca on Mastodon: https://mas.to/@gianlubaio
- Gianluca on Twitter: https://twitter.com/gianlubaio
- Gianluca on Linkedin: https://www.linkedin.com/in/gianluca-baio-b893879/
- Gianluca’s articles on arXiv: https://arxiv.org/a/baio_g_1.html
- R for Health Technology Assessment (HTA) Consortium: https://r-hta.org/
- LBS #50 – Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
- LBS #45 – Biostats & Clinical Trial Design, with Frank Harrell: https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/
- How to find priors intuitively= https://www.youtube.com/watch?v=9shZeqKG3M0
- Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3I
- LBS Topical Playlists (also available as RSS feeds on the website): https://www.youtube.com/@learningbayesianstatistics8147/playlists
Abstract
In this week’s episode, I talk to Gianluca Baio. He is the head of the department of Statistical Science at University College London and earned a MA and PhD in Florence in Statistics and Economics.
His work primarily focuses on Bayesian modeling for decision making in healthcare, for example in case studies for novel drugs and whether this alternative treatment is worth the cost. Being a relatively young field, health economics seems more open to Bayesian statistics than more established fields.
While Bayesian statistics becomes more common in clinical trial research, many regulatory bodies still prefer classical p-values. Nonetheless, a lot of COVID modelling was done using Bayesian statistics.
We also talk about the purpose of statistics, which is not to prove things but to reduce uncertainty.
Gianluca explains that proper communication is important when eliciting priors and involving people in model building.
The future of Bayesian statistics is that statistics should have more primacy, and he hopes that statistics will stay central rather than becoming embedded in other approaches like data science, notwithstanding, communication with other disciplines is crucial.
Transcript
Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.
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