Hierarchical Bayesian Benefit-Risk Assessment Using Discrete
Choice Experiment
c(person("Saurabh", "Mukhopadhyay", comment = "AbbVie Inc.",
role = c("aut"), email = "saurabh.mukhopadhyay@abbvie.com"),
person("Saurabh", "Mukhopadhyay",
role = c( "cre"), email = "stat.mukherjee@gmail.com"))
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.
Package 'R2jags' requires JAGS executable file installed
Saurabh Mukhopadhyay [aut] (AbbVie Inc.),
Saurabh Mukhopadhyay [cre]

Hierarchical Bayesian Design for Clinical Trials
Implements our hierarchical Bayesian design for randomized clinical trials.

Hill/Bliss Independence Model for Combination Vaccines
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.

Calculate expected relative risk and proportion protected
assuming normally distributed log10 transformed antibody dose
for several component vaccine. Uses Hill models for each
component which are combined under Bliss independence.

Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

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Hierarchial Bayesian Analysis of Recognition Memory
Contains functions for fitting hierarchial versions of
EVSD, UVSD, and DPSD to recognition memory confidence-ratings
data.