Adaptation of Virtual Twins method from Jared Foster
Ви не можете вибрати більше 25 тем Теми мають розпочинатися з літери або цифри, можуть містити дефіси (-) і не повинні перевищувати 35 символів.
prise6 e78be83e6e
Merge pull request #2 from prise6/fix-cran1
1 рік тому
R fix incidence table 1 рік тому
data Update manual 4 роки тому
data-raw cran comments 2 роки тому
man Make example checkable + delete import rpart function 1 рік тому
revdep Add 'rmarkdown' in suggest field + update infos 1 рік тому
vignettes delete toc 2 роки тому
.Rbuildignore Add 'rmarkdown' in suggest field + update infos 1 рік тому
.gitignore Add 'rmarkdown' in suggest field + update infos 1 рік тому
DESCRIPTION Add infos about Jared Foster 1 рік тому
LICENSE Ajout des fichiers de suivis 2 роки тому
NAMESPACE Ajout des fichiers de suivis 2 роки тому
NEWS Add 'rmarkdown' in suggest field + update infos 1 рік тому
README.md add e1071 to suggest field 1 рік тому
VirtualTwins.Rproj Change options values 4 роки тому
cran-comments.md submission 4 fix table 1 рік тому

README.md

aVirtualTwins

CRAN_Status_Badge

An adaptation of VirtualTwins method from Foster, J. C., Taylor, J. M.G. and Ruberg, S. J. (2011)

VirtualTwins is a method of subgroup identification from randomized clinical trial data.

In 2015, as an intern in a french pharmaceutical group, i worked on this method and develop a package based on Jared Foster and al method.

(Very) Quick Preview

# Load data
data(sepsis)
# Format data
vt.obj <- vt.data(dataset         = sepsis,
                  outcome.field   = "survival",
                  treatment.field = "THERAPY",
                  interactions    = TRUE)
# Print Incidences of sepsis data
vt.obj$getIncidences()
# $table
#            trt
# resp        0    1     sum  
#   0         101  188   289  
#   1         52   129   181  
#   sum       153  317   470  
#   Incidence 0.34 0.407 0.385
#
# $rr
# [1] 1.197059
#
# First step : create random forest model
vt.for <- vt.forest(forest.type  = "one",
                    vt.data      = vt.obj,
                    interactions = TRUE,
                    ntree        = 500)
# Second step : find rules in data 
vt.trees <- vt.tree(tree.type = "class",
                    vt.difft  = vt.for, 
                    threshold = quantile(vt.for$difft, seq(.5,.8,.1)),
                    maxdepth  = 2)
# Print results
vt.sbgrps <- vt.subgroups(vt.trees)
knitr::kable(vt.sbgrps)
Subgroup Subgroup size Treatement event rate Control event rate Treatment sample size Control sample size RR (resub) RR (snd)
tree1 PRAPACHE>=26.5 157 0.752 0.327 105 52 2.300 1.856
tree3 PRAPACHE>=26.5 & AGE>=51.74 120 0.897 0.31 78 42 2.894 1.991

 Infos

Currently this package works for RCT with two treatments groups and binary outcome.

Most of the package use Reference Class programing (in R). Feel free to create your own classes.

Of course, subgroup identification in general with two treatment and severals group can be possible.

Help & Documentation

See wiki tab.

Or:

vignette("full-example", package = "aVirtualTwins")

Or:

Here’s a link to my intern dissertation (french version) La recherche de sous-groupes par Virtual Twins (parts V & VI).

Install

# use devtools library
library(devtools)
# install from github
devtools::install_github("prise6/aVirtualTwins", build_vignettes = TRUE)
# load library
library(aVirtualTwins)

To-do list

  • Link to my simulation
  • Submit to CRAN
  • Use R6 for perfs issues
  • Vignette on-line

News

See NEWS file

Contact

vieille.francois at gmail.com