Speaker
Description
The pharmaceutical industry has a long history with Bayesian statistics. Already in 1986, Racine, Grieve, Flühler and Smith wrote an article entitled Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry[1], highlighting four typical applications they encountered at that time. Since then, Bayesian methods have been applied to many more problems in the pharmaceutical industry, and statisticians working in this industry have made substantial contributions leading to important advancements in the field. Some particularly appealing areas for invoking the Bayesian approach are (a) prediction problems such as probability of success; (b) multiple imputation; (c) data-sparse problems (as in phase I oncology studies); (d) shrinkage estimation; and (e) the use of external information. While the use of Bayesian statistics for (a)–(c) is widely accepted nowadays, (d) and especially (e) have led to much more controversial discussions. In the confirmatory setting, the strong focus on (frequentist) error control has proven to be a high hurdle, thus limiting even the consideration of Bayesian designs for this purpose.
In this talk, I will discuss some of the successes (the “Highs”) of Bayesian statistics in the pharmaceutical industry, while also elaborating on some of the real (or perceived) challenges (the “Lows”). I will enrich these reflections with my personal perspective on uncertainty quantification and why I believe that the Bayesian framework provides a unique advantage in this regard.
[1] Racine, A., Grieve, A. P., Flühler, H., & Smith, A. F. M. (1986). Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry. Journal of the Royal Statistical Society: Series C (Applied Statistics), 35(2), 93-120.
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