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bipd (version 0.3)

Bayesian Individual Patient Data Meta-Analysis using 'JAGS'

Description

We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) . This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

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Version

Install

install.packages('bipd')

Monthly Downloads

322

Version

0.3

License

GPL-3

Maintainer

Michael Seo

Last Published

June 5th, 2022

Functions in bipd (0.3)

ipdma.impute

Impute missing data in individual participant data with two treatments (i.e. placebo and a treatment).
ipdma.model.deft.onestage

Make a (deft-approach) one-stage individual patient data meta-analysis object containing data, priors, and a JAGS model code
ipd.run

Run the model using the ipd object
findMissingPattern

Find missing data pattern in a given data
ipd.run.parallel

Run the model using the ipd object with parallel computation
generate_ipdma_example

Generate a simulated IPD-MA data for demonstration
add.mcmc

Convenient function to add results (i.e. combine mcmc.list)
bipd-package

bipd: A package for individual patient data meta-analysis using 'JAGS'
ipdma.model.onestage

Make an one-stage individual patient data meta-analysis object containing data, priors, and a JAGS model code
treatment.effect

Calculate patient-specific treatment effect
ipdnma.model.onestage

Make an one-stage individual patient data network meta-analysis object containing data, priors, and a JAGS model code
generate_ipdnma_example

Generate a simualted IPD-NMA data for demonstration
generate_sysmiss_ipdma_example

Generate a simulated IPD-MA data with systematically missing covariates