Package 'RcmdrPlugin.RMTCJags'

Title: R MTC Jags 'Rcmdr' Plugin
Description: Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Authors: Marcelo Goulart Correia <[email protected]>
Maintainer: Marcelo Goulart Correia <[email protected]>
License: GPL (>= 2)
Version: 1.0-2
Built: 2024-11-20 06:56:15 UTC
Source: CRAN

Help Index


R MTC Jags Rcmdr Plugin

Description

Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an Rcmdr plug-in to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).

Details

Package: RcmdrPlugin.RMTCJags
Type: Package
Version: 1.01-1
Date: 2015-06-17
License: GPL (>= 2)

Author(s)

Marcelo Goulart Correia <[email protected]>

See Also

Rcmdr.


How to format database for analysis?

Description

Manual to build database for RcmdrPlugin.RMTCJags

Details

Fixed Effect Model (FE Model), Random Effect Model (RE Model) Ignoring multi-arm trials and Random Effect Model (RE Model) for 2- and 3-arms trials:

A database with six (6) variables:
s -> Study index (Number)
t -> Treatment index (Number)
r -> Number of cases on the treatment
n -> Total population on the treatment
b -> Baseline treatment
m -> Arm index (Only needed on RE Model for 2- and 3-arms trials), where 1 is the baseline treatment and 2,..,n are for the other treatments

Each line on the database is a treatment of a trial (study), for example:

s t r n b m
1 1 40 100 1 1
1 3 15 90 1 2
1 4 10 75 1 3
... ... ... ... ... ...
4 2 50 200 2 1
4 4 60 150 2 2

Random Effect Model (RE Model) for multi-arm trial:

A database with N*3 + 1 columns, where N is the highest number of arms from a trial collection.

t[1,..N,] -> Treatment index
r[1,..N,] -> Number of cases on the treatment
n[1,..N,] -> Total population on the treatment
na -> Number of arms on the study

Each line on the database is a trial. For example, if we collect 10 trials and after check them we have the biggest trial with 5 arms our database structure is:

t[1,] t[2,] t[3,] t[4,] t[5,] r[1,] r[2,] r[3,] r[4,] r[5,] n[1,] n[2,] n[3,] n[4,] n[5,] na
1 2 3 4 5 20 30 10 5 14 100 90 80 110 50 5
1 3 4 5 NA 10 50 60 15 NA 150 200 340 165 1 4
2 4 5 NA NA 40 70 80 NA NA 70 190 500 1 1 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3 4 NA NA NA 80 90 NA NA NA 250 580 1 1 1 2