Principal stratification used to play a role only in observational research but at least since the addendum of the ICH E9 guideline, this approach to causal inference became a hot topic.
In this episode, I talk with 2 experts from Novartis and Roche. We cover the following questions:
- What is Principal Stratification?
- How would you describe principal stratification to a non-statistician?
- Where do you see the benefits of this estimand compared to the other typical strategies?
- Which critique points do are usually raised against this approach?
- How do you implement/calculate corresponding estimates for this estimand?
- What references would you recommend for further reading?
Björn and Kaspar recommend the following very useful references:
- Books:
- Introduction into potential outcomes and causal inference: https://www.cambridge.org/core/books/causal-inference-for-statistics-social-and-biomedical-sciences/71126BE90C58F1A431FE9B2DD07938AB
- Hernan and Robins: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
- Pearl, the book of why: https://www.amazon.de/Book-Why-Science-Cause-Effect/dp/046509760X
- Papers:
- Paper draft: https://arxiv.org/abs/2008.05406 with markdown: https://oncoestimand.github.io/princ_strat_drug_dev/princ_strat_example.html and github: https://github.com/oncoestimand/princ_strat_drug_dev
- Magnusson et al (Siponimod Beispiel): https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8333 and the corresponding EPAR: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-evaluation-anticancer-medicinal-products-man-revision-5_en.pdf
- Oncology estimand working group: http://www.oncoestimand.org/
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Kaspar Rufibach
Kaspar is an Expert Statistical Scientist in Roche’s Methods, Collaboration, and Outreach group and located in Basel.
He does methodological research, provides consulting to Roche statisticians and broader project teams, gives biostatistics trainings for statisticians and non-statisticians in- and externally, mentors students, and interacts with external partners in industry, regulatory agencies, and the academic community in various working groups and collaborations.
He has co-founded and co-leads the European special interest group “Estimands in oncology” (sponsored by PSI and EFSPI, which also has the status as an ASA scientific working group, a subsection of the ASA biopharmaceutical section) that currently has 39 members representing 23 companies, 3 continents, and several Health Authorities. The group works on various topics around estimands in oncology.
Kaspar’s research interests are methods to optimize study designs, advanced survival analysis, probability of success, estimands and causal inference, estimation of treatment effects in subgroups, and general nonparametric statistics. Before joining Roche, Kaspar received training and worked as a statistician at the Universities of Bern, Stanford, and Zurich.
More on the oncology estimand WG: http://www.oncoestimand.org
More on Kaspar: http://www.kasparrufibach.ch
Björn Bornkamp
Statistical Methodologist at Novartis
Björn Bornkamp works in the Statistical Methodology Group at Novartis in Basel, where he provides consulting to statisticians and clinical teams on topics related to dose-finding studies, subgroup analyses, Bayesian statistics as well as estimands and causal inference.
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