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BayesOrdDesign (version 0.1.2)

Bayesian Group Sequential Design for Ordinal Data

Description

The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) . Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) . Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) .

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Version

Install

install.packages('BayesOrdDesign')

Monthly Downloads

44

Version

0.1.2

License

GPL-2

Maintainer

Chengxue Zhong

Last Published

November 14th, 2022

Functions in BayesOrdDesign (0.1.2)

rjmcmc_func

Perform reversible-jump MCMC post-process to select appropriate model between proportional odds (PO) model and non-proportional odds (NPO) model
ss_npo

Determine the sample size for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption
Bayes_ord

Bayesian ordinal regression analysis Estimate the correlation coefficients of treatment variable, with and without the proportional odds assumption
get_oc_Switch

Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption.
ss_switch

Determine the sample size for Bayesian two-stage trial design for ordinal endpoints based on switch model
example.data

Clinical ordinal endpoints and treatments assignment for 200 patient
get_oc_PO

Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption
get_oc_NPO

Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption
ss_po

Determine the sample size for Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption