# 13 Resources

## Interactive Demonstrations

## Probability

- Let’s talk about probability 25m video by Bill Press (
**highly recommended**) - Introduction to Probability, a free online course from Harvard by Joseph Blitzstein
- Introduction to probability and statistics from MIT by Jeremy Orloff and Jonathan Bloom

## General

- Application of Bayesian approaches in drug development: starting a virtuous cycle by Steve Ruberg et al
- Statistical Remedies for Medical Researchers by Peter Thall
- A gentle introduction to the comparison between null hypothesis testing and Bayesian analysis: Reanalysis of two randomized controlled trials by Marcus Bendtsen
- Introductory Video: Frequentist vs. Bayes
- Resources from the DIA Bayesian Scientific Working Group
- Bayes 32m video by Bill Press
- Bayesian statistics without frequentist language talk by Richard McElreath
- Bayesian statistics for the social sciences by Ben Goodrich (provides a nice background to logic, probability as an extension to logic, the different schools of statistical inference, and subjectivity/objectivity)
- Thomas Bayes and the crisis in science by David Papineau
- How to become a Bayesian in eight easy steps annotated reading list
- Web resource for Bayesian methods in drug development especially for Phase 1 and 2 studies
- R scripts illustrating Bayesian analyses
- Bayesian estimation supersedes the t test by John Kruschke
- Bayesian t-tests
- Michael Clark’s Bayesian Basics
- Michael Clark’s Become a Bayesian with R and Stan
- Scripts with nice workflow for analyzing fentanyl trends using multilevel Bayesian models
- Bayesian sequential testing calculator
- Bayesian posteriors are calibrated by definition by Andrew Gelman
- Verifying posterior distribution calculations
- Bayesian statistics without frequentist language video by Richard McElreath
- Tutorials from StanCon 2018 Helsinki
- Bayesian perspective on proposed FDA adaptive trial guideline
- A student’s guide to Bayesian statistics by Ben Lambert
- Book for Lambert’s
*A Student’s Guide to Bayesian Statistics* - Data analysis: A Bayesian tutorial by Devinderjit Sivia and John Skilling
- What is Bayesian/frequentist inference? by Larry Wasserman
- Lawrence Joseph’s Bayesian courses
- Analytix Thinking Blog by Steve Ruberg
- Bayesian Inference 54m video by Ben Vincent
- Bayesian Inference is Just Counting 2h video by Richard McElreath
- Teaching Bayesian and Frequentist Methods Side by Side 1 hour video by John Kruschke, and presentation and interactive app
- Détente: A Practical Understanding of P‐values and Bayesian Posterior Probabilities by Stephen Ruberg
- Bayesian Data Analysis by Gelman et al (book available for free for non-commercial purposes)
- Improving transparency and replication in Bayesian statistics: The WAMBS checklist by Depaoli and van de Schoot
- hbiostat.org/bayes
- Reasons confidence intervals and credible intervals may not be equated by Ladislas Nalborczyk, Paul-Christian Bürkner, Donald R. Williams
- Bayesian workflow by A Gelman et al
- Bayesian methods in human drug and biological products development in CDER and CBER

## Choice of Priors

- Prior choice recommendations wiki
- How are Bayesian priors decided in real life?
- Zero-excluding priors for variance parameters
- Prior distributions and the Australia principle
- There is always prior information by Richard McElreath
- What are credible priors and what are skeptical priors? - discussion on datamethods.org
- A weakly informative default prior distribution for logistic and other regression models
- Predictively consistent prior effective sample sizes by Beat Neuenschwander et al
- Quantification of prior impact in terms of effective current sample size by Manuel Wiesenfarth and Silvia Calderazzo
- Is ignorance bliss? by Stephen Senn (describes logic of linking random effects variance to size of average treatment effect)
- Why optional stopping can be a problem for Bayesians by Rianne de Heide and Peter Grünwald. (has an interesting taxonomy of types of priors)

## Design and Sample Size

- A review of Bayesian perspectives on sample size derivation for confirmatory trials by K Kunzmann et al.
- Joint control of consensus and evidence in Bayesian design of clinical trials by F De Santis and S Gubbiotti. Covers how to compute a sample size to achieve consensus in posteriors when priors conflict.
- Bayesian power analysis by A. Solomon Kurz
- Improving clinical trials using Bayesian adaptive designs: a breast cancer example by Wei Hong et al.

## Multiplicity

- Bayesian multiplicity control by Jim Berger. Contains a formal proof that sequential monitoring requires no multiplicity adjustment in the Bayesian context (slide 30).
- Of Amazons, Martingales, rogue traders and sequential analysis by Stephen Senn
- Interim analysis for early stopping during the study by David Bock
- Why optional stopping can be a problem for Bayesians by Rianne de Heide and Peter Grünwald. (But focuses on Bayes’ factors)
- Bayesian inference completely solves the multiple comparisons problem by Andrew Gelman

`brms`

Package in R (uses Stan)

- Statistical Rethinking examples with R brms
- Statistical Rethinking with brms, ggplot2, and the tidyverse by AS Kurz
- Blog posts about the R brms package for regression models using Stan
- Handling missing values with brms
- brms vignettes
- Generalized additive models
- Ordinal predictors
- Hands-on example of Bayesian mixed models with brms by Andrey Anikin

## Other Software

- Plotting Posterior Distributions with ggdistribute
- Building Models in PyMC3
- Simulation Practices for Adaptive Trial Designs in Drug and Device Development by Mayer et al
- Bayesian survival analysis using the
`rstanarm`

R package - When MCMC fails: The advice we’re giving is wrong. Here’s what we you should be doing instead by Andrew Gelman

## Regression Modeling

## Reporting and Graphics for Bayesian Analyses

- Tidy data and Bayesian analysis make uncertainty visualization fun by Matthew Kay
- Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models
- Bayesian reanalyses from summary statistics: A guide for academic consumers (perhaps too much emphasis on Bayes factors and point hypotheses)

## Philosophy

- Properties of uncertainty by Jason Wilkes
- Why is it so hard to do good science? by Ray Dingledine; discusses decision making and downsides of not using Bayesian thinking
- The Flawed Reasoning Behind the Replication Crisis by Aubrey Clayton