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Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.

License: GNU General Public License v3.0

R 53.47% C++ 42.04% C 0.75% TeX 1.54% Batchfile 0.71% Shell 1.48%

c212's Introduction

output
github_document

c212: Methods for Detecting Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

The goal of c212 is to provide a self-contained set of methods, which use groupings of adverse events, to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events.

Installation

You can install the released version of c212 from CRAN with:

install.packages("c212")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("rcarragh/c212")

Example

This is a basic example which shows how to apply the Double False Discovery Rate to a set of multiple hypotheses:

library(c212)
data(c212.FDR.data)
c212.err.cntrl(c212.FDR.data, method="DFDR", alpha = 0.05)
#>           B j         AE        p
#> 1 Bdy-sys_3 1   Adv-Ev_6 0.000000
#> 2 Bdy-sys_3 2   Adv-Ev_7 0.000011
#> 3 Bdy-sys_3 3   Adv-Ev_8 0.000021
#> 4 Bdy-sys_3 5 Adv-Ev_910 0.000039
#> 5 Bdy-sys_3 6 Adv-Ev_911 0.000079
#> 6 Bdy-sys_3 7 Adv-Ev_912 0.003554
#> 7 Bdy-sys_3 4   Adv-Ev_9 0.010411

This is an example of how to apply the Berry and Berry model:

library(c212)
data(c212.trial.data)
mod.BB <- c212.BB(c212.trial.data, burnin = 100, iter = 200)
#> Global Simulation Parameters:
#> 	Simulation Type: 1
#> 	w_alpha (width): 1.000000
#> 	m alpha (control): 6.000000
#> 	w_beta (width): 1.000000
#> 	m beta (control): 6.000000
#> 	w_gamma (width): 1.000000
#> 	m gamma (control): 6.000000
#> 	sigma_MH_alpha: 3.000000
#> 	sigma_MH_beta: 3.000000
#> 	sigma_MH_gamma: 0.200000
#> 	sigma_MH_theta: 0.200000
#> 	default weight: 0.500000
#> MCMC chain fitting complete.
#> [1] "MCMC fitting complete."

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