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BayesPPD (version 1.1.3)

Bayesian Power Prior Design

Description

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) .

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Version

Install

install.packages('BayesPPD')

Monthly Downloads

305

Version

1.1.3

License

GPL (>= 3)

Maintainer

Yueqi Shen

Last Published

January 13th, 2025

Functions in BayesPPD (1.1.3)

power.two.grp.random.a0

Power/type I error calculation for two groups (treatment and control group, no covariates) with random a0
power.two.grp.fixed.a0

Power/type I error calculation for data with two groups (treatment and control group, no covariates) with fixed a0
normalizing.constant

Function for approximating the normalizing constant for generalized linear models with random a0
BayesPPD-package

Bayesian sample size determination using the power and normalized power prior for generalized linear models
actg036

AIDS Clinical Trial ACTG036 (1991).
glm.random.a0

Model fitting for generalized linear models with random a0
glm.fixed.a0

Model fitting for generalized linear models with fixed a0
power.glm.random.a0

Power/type I error calculation for generalized linear models with random a0
power.glm.fixed.a0

Power/type I error calculation for generalized linear models with fixed a0
actg019

AIDS Clinical Trial ACTG019 (1990).
two.grp.fixed.a0

Model fitting for two groups (treatment and control group, no covariates) with fixed a0
two.grp.random.a0

Model fitting for two groups (treatment and control group, no covariates) with random a0