[R-pkgs] aVirtualTwins available on CRAN

[Francois vieille]

[markdown format]

I'm glad to introduce you the new package aVirtualTwins. This package is an adaptation of VirtualTwins method of subgroup identification from [Foster, J. C., Taylor, J. M.G. and Ruberg, S. J. (2011)](http://onlinelibrary.wiley.com/doi/10.1002/sim.4322/abstract).

### Explanation

Virtual Twins has been created to find subgroup of patients in a random clinical trial with enhanced treatment effect, if it exists. Theorically, this method can be used for binary and continous outcome. This package only deals with binary outcome in a two arms clinical trial.

Virutal Twins is also adapted for A/B testing of course.

Virtual Twins is based on random forest and regression/classification trees.

### Quick preview

Here's a example of aVirtualTwins use with a well known dataset (_sepsis_) in subgroup decovery:

_Sepsis_ contains simulated data on 470 subjects with a binary outcome survival, that stores survival status for patient after 28 days of treatment, value of 1 for subjects who died after 28 days and 0 otherwise. There are 11 covariates, listed below, all of which are numerical variables.


```r
library(aVirtualTwins)

# Load data
data(sepsis)
# Format data
vt.obj <- vt.data(dataset         = sepsis,
                  outcome.field   = "survival",
                  treatment.field = "THERAPY",
                  interactions    = TRUE)
## "1" will be the favorable outcome
# view of data
head(sepsis)
##   survival THERAPY PRAPACHE    AGE BLGCS ORGANNUM   BLIL6  BLLPLAT
## 1        0       1       19 42.921    15        1  301.80 191.0000
## 2        1       1       48 68.818    11        2  118.90 264.1565
## 3        0       1       20 68.818    15        2   92.80 123.0000
## 4        0       1       19 33.174    14        2 1232.00 244.0000
## 5        0       1       48 46.532     3        4 2568.00  45.0000
## 6        0       0       21 56.098    14        1  162.65 137.0000
##    BLLBILI BLLCREAT TIMFIRST BLADL blSOFA
## 1 2.913416 1.000000    17.17     0   5.00
## 2 0.400000 1.100000    17.17     5  10.00
## 3 5.116471 1.000000    10.00     1   7.50
## 4 3.142092 1.200000    17.17     0   6.25
## 5 4.052668 3.000000    10.00     0  12.00
## 6 0.500000 4.662556    10.00     0   8.75
# 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
# $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      1.774
tree3   PRAPACHE>=26.5 & AGE>=51.74   120             0.897                   0.31                 78                      42                          2.894      1.924


aVirtualTwins can be found on [CRAN](https://cran.r-project.org/package=aVirtualTwins) and [github](https://github.com/prise6/aVirtualTwins). Feel free to contribute.

Francois.