CE_27C: Informative Prior Elicitation Using Historical Data, Expert Opinion, and Other Sources (Added Fee)

This full-day short course is designed to give biostatisticians and data scientists a comprehensive overview of informative prior elicitation from historical data, expert opinion, and other data sources, such as real-world data, prior predictions, estimates, and summary statistics. We focus both on Bayesian design and analysis and examples will be presented for several types of applications such as clinical trials, observational studies, environmental studies as well as other areas in biomedical research. The methods we present will be demonstrated Stan, SAS, and the newly developed R packages hdbayes and BayesPPD. The first part of the course gives a brief but broad overview of Bayesian inference, examining concepts of Bayesian design and analysis such as i) Bayesian type 1 error and power, ii) calculation of posterior and predictive distributions, iii) MCMC sampling methods, iv) fundamental concepts in informative and non-informative prior elicitation, v) Bayesian point and interval estimation, and vi) Bayesian hypothesis testing. These topics will be presented in a general context as well in several contexts in regression settings including linear and generalized linear models, models for longitudinal data, and survival models. The first part of the course contains two sections. The second part of the course will focus broadly on advanced methods for informative prior elicitation, including i) informative prior elicitation from historical data using the power prior (PP) and its variations including the normalized power prior, the partial borrowing power prior, the asymptotic power prior, and the scale transformed power prior (STRAPP). In addition, ii) the Bayesian hierarchical mode (BHM) commensurate prior, and the robust Meta-analytic Mixture Prior (MAP) will also be examined and the properties and performance of the four priors (BHM, PP, commensurate, robust MAP) will be analytically compared and studied via simulations and real data analyses of case studies. In addition, we will also examine iii) informative prior elicitation from predictions, including the hierarchical prediction prior (HPP), and the Information Matrix (IM) prior. We also examine iv) strategies for informative prior elicitation from expert opinion. Finally, we discuss (v) synthesis of randomized controlled trial and real-world data using Bayesian nonparametric methods. For (i) – (iv), we will present examples both in the context of Bayesian design and analysis and demonstrate the performance of these prior through several simulation studies and case studies involving real data in the context of linear and generalized linear models, longitudinal data, and survival data. We will also demonstrate the implementation of these priors through the hdbayes and BayesPPD R packages, SAS, Nimble, and Stan. The second part of the course consists of two sections. The first 4 hours of the course will be split into two sections, and the last 4 hours of the course will also be split into two sections as follows: