A Markov Chain Monte-Carlo Approach to Dose-Response Optimization Using Probabilistic Programming (RStan)
Applications
2020-12-01 v1
Abstract
A hierarchical logistic regression Bayesian model is proposed and implemented in R to model the probability of patient improvement corresponding to any given dosage of a certain drug. RStan is used to obtain samples from the posterior distributions via Markov Chain Monte-Carlo (MCMC) sampling. The effects of selecting different families of prior distributions are examined and finally, the posterior distributions are compared across RStan, and two other environments, namely PyMC, and AgenaRisk.
Cite
@article{arxiv.2011.15034,
title = {A Markov Chain Monte-Carlo Approach to Dose-Response Optimization Using Probabilistic Programming (RStan)},
author = {Dorsa Mohammadi Arezooji},
journal= {arXiv preprint arXiv:2011.15034},
year = {2020}
}
Comments
12 pages, 9 figures "for code source, see https://github.com/Dorsa-Arezooji/Sick-Pigs"