| Title: | Adaptation of Virtual Twins Method from Jared Foster |
|---|---|
| Description: | Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>). |
| Authors: | Francois Vieille [aut, cre], Jared Foster [aut] |
| Maintainer: | Francois Vieille <[email protected]> |
| License: | GPL-3 | file LICENSE |
| Version: | 1.0.1 |
| Built: | 2025-03-08 03:01:15 UTC |
| Source: | https://github.com/prise6/avirtualtwins |
aVirtualTwins is written mainly with reference classes. Briefly, there is three kinds of class :
VT.object class to represent RCT dataset used by aVirtualTwins. To format correctly RCT dataset, use formatRCTDataset.
VT.difft class to compute difference between twins. Family VT.forest extends it to compute twins by random forest.
vt.forest is users function.
VT.tree class to find subgroups from difft by CART trees. VT.tree.class and VT.tree.reg extend it.
vt.tree is users function.
See http://github.com/prise6/aVirtualTwins for last updates.
formatRCTDataset returns dataset that Virtual Twins is able to
analyze.
formatRCTDataset(dataset, outcome.field, treatment.field, interactions = TRUE)formatRCTDataset(dataset, outcome.field, treatment.field, interactions = TRUE)
dataset |
data.frame representing RCT's |
outcome.field |
name of the outcome's field in |
treatment.field |
name of the treatment's field in |
interactions |
logical. If running VirtualTwins with treatment's interactions, set to TRUE (default value) |
This function check these differents topic: Outcome must be binary and a
factor. If numeric with two distincts values, outcome becomes a factor where
the favorable reponse is the second level. Also, outcome is moved on the
first column of dataset.
Treatment must have two distinct numeric values, 0 : no treatment, 1 : treatment. Treatment is moved to the second column.
Qualitatives variables must be factor. If it has more than two levels, if running VirtualTwins with interaction, it creates dummy variables.
return data.frame with good format (explained in details section) to run VirtualTwins
## Not run: data.format <- formatRCTDataset(data, "outcome", "treatment", TRUE) ## End(Not run) data(sepsis) data.format <- formatRCTDataset(sepsis, "survival", "THERAPY", T)## Not run: data.format <- formatRCTDataset(data, "outcome", "treatment", TRUE) ## End(Not run) data(sepsis) data.format <- formatRCTDataset(sepsis, "survival", "THERAPY", T)
Simulated clinical trial with two groups treatment about sepsis desease. See details.
data(sepsis)data(sepsis)
470 patients and 13 variables.
binary outcome
1 for active treatment, 0 for control treatment
Time from first sepsis-organ fail to start drug
Patient age in years
Baseline local platelets
Sum of baselin sofa (cardiovascular, hematology, hepaticrenal, and respiration scores)
Base creatinine
Number of baseline organ failures
Pre-infusion apache-ii score
Base GLASGOW coma scale score
Baseline serum IL-6 concentration
Baseline activity of daily living score
Baseline local bilirubin
This dataset is taken from SIDES method.
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.
Note that contrary to the original dataset used in SIDES, missing values have
been imputed by random forest (randomForest::rfImpute()). See file
data-raw/sepsis.R for more details.
True subgroup is PRAPACHE <= 26 & AGE <= 49.80. NOTE: This subgroup is defined with the lower event rate (survival = 1) in treatement arm.
http://biopharmnet.com/subgroup-analysis-software/
vt.data is a wrapper of formatRCTDataset and
VT.object. Allows to format your data.frame in order to create
a VT.object object.
vt.data(dataset, outcome.field, treatment.field, interactions = TRUE, ...)vt.data(dataset, outcome.field, treatment.field, interactions = TRUE, ...)
dataset |
data.frame representing RCT's |
outcome.field |
name of the outcome's field in |
treatment.field |
name of the treatment's field in |
interactions |
logical. If running VirtualTwins with treatment's interactions, set to TRUE (default value) |
... |
parameters of |
VT.object
data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T)data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T)
A reference class to represent difference between twin1 and twin2
Difft are calculated depending on the favorable outcome chosen. It is the second level of the outcome. For example, if the outcome is 0 and 1, the favorable outcome is 1. Then,
. So absolute method is :
So relative method is :
So absolute method is :
vt.objectVT.object (refClass) representing data
twin1vector of
twin2vector of
methodMethod available to compute difft : c("absolute", "relative", "logit"). Absolute is default value. See details.
difftvector of difference between twin1 and twin2
computeDifft()Compute difference between twin1 and twin2. See details.
VT.forest, VT.forest.one,
VT.forest.double
vt.forest is a wrapper of VT.forest.one,
VT.forest.double and VT.forest.fold. With
parameter forest.type, any of these class can be used with its own parameter.
vt.forest(forest.type = "one", vt.data, interactions = T, method = "absolute", model = NULL, model_trt1 = NULL, model_trt0 = NULL, ratio = 1, fold = 10, ...)vt.forest(forest.type = "one", vt.data, interactions = T, method = "absolute", model = NULL, model_trt1 = NULL, model_trt0 = NULL, ratio = 1, fold = 10, ...)
forest.type |
must be a character. "one" to use VT.forest.one class. "double" to use VT.forest.double. "fold" to use VT.forest.fold. |
vt.data |
|
interactions |
logical. If running VirtualTwins with treatment's interactions, set to TRUE (default value) |
method |
character c("absolute", "relative", "logit"). See
|
model |
allows to give a model you build outside this function. Can be randomForest, train or cforest. Is only used with forest.type = "one". If NULL, a randomForest model is grown inside the function. NULL is default. |
model_trt1 |
see model_trt0 explanation and
|
model_trt0 |
works the same as model parameter. Is only used with
forest.type = "double". If NULL, a randomForest model is grown inside the
function. NULL is default. See |
ratio |
numeric value that allow sampsize to be a bit controlled.
Default to 1. See |
fold |
number of fold you want to construct forest with k-fold method.
Is only used with forest.type = "fold". Default to 5. See
|
... |
randomForest() function parameters. Can be used for any forest.type. |
VT.difft
data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T) # inside model : vt.f <- vt.forest("one", vt.o) # ... # your model : # library(randomForest) # rf <- randomForest(y = vt.o$getY(), # x = vt.o$getX(int = T), # mtry = 3, # nodesize = 15) # vt.f <- vt.forest("one", vt.o, model = rf) # ... # Can also use ... parameters vt.f <- vt.forest("one", vt.o, mtry = 3, nodesize = 15) # ...data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T) # inside model : vt.f <- vt.forest("one", vt.o) # ... # your model : # library(randomForest) # rf <- randomForest(y = vt.o$getY(), # x = vt.o$getX(int = T), # mtry = 3, # nodesize = 15) # vt.f <- vt.forest("one", vt.o, model = rf) # ... # Can also use ... parameters vt.f <- vt.forest("one", vt.o, mtry = 3, nodesize = 15) # ...
An abstract reference class to compute twin via random forests
VT.forest extends VT.difft
...see fields of VT.difft
checkModel(model)Checking model class: Must be : train, RandomForest, randomForest
getFullData()Return twin1, twin2 and difft in column
run()Compute twin1 and twin2 estimation. Switch treatment if necessary.
VT.difft, VT.forest.one, VT.forest.double
A reference class to compute twins via double random forests
VT.forest.double extends VT.forest.
if is estimated by OOB predictions from
model_trt1.
if is estimated by OOB predictions from
model_trt0.
This is what computeTwin1() does.
Then if is estimated by model_trt1.
Then if is estimated by model_trt1.
This is what computeTwin2() does.
model_trt1a caret/RandomForest/randomForest object for treatment T = 1
model_trt0a caret/RandomForest/randomForest object for treatment T = 0
...field from parent class : VT.forest
computeTwin1()Compute twin1 with OOB predictions from double forests. See details.
computeTwin2()Compute twin2 by the other part of data in the other forest. See details.
VT.difft, VT.forest,
VT.forest.one
A reference class to compute twins via k random forest
VT.forest.fold extends VT.forest
Twins are estimated by k-fold cross validation. A forest is computed on k-1/k of the data and then used to estimate twin1 and twin2 on 1/k of the left data.
interactionslogical set TRUE if model has been computed with interactions
foldnumeric, number of fold, i.e. number of forest (k)
rationumeric experimental, use to balance sampsize. Defaut to 1.
groupsvector Define which observations belong to which group
...field from parent class : VT.forest
run()Compute twin1 and twin2 estimation. Switch treatment if necessary.
VT.difft, VT.forest,
VT.forest.one, VT.forest.double
A reference class to compute twins via one random forest
VT.forest.one extends VT.forest.
OOB predictions are used to estimate . Then,
treatement is switched, it means that 1 becomes 0 and 0 becomes 1. We use
again model to estimate . This is
what computeTwin1() and computeTwin2() functions do.
modelis a caret/RandomForest/randomForest class object
interactionslogical set TRUE if model has been computed with interactions
...field from parent class : VT.forest
computeTwin1()Compute twin1 with OOB predictions
computeTwin2()Compute twin2 by switching treatment and applying random forest model
VT.difft, VT.forest, VT.forest.double
A Reference Class to deal with RCT dataset
Currently working with binary response only. Continous will come, one day. Two-levels treatment only as well.
data field should be as described, however if virtual twins won't used
interactions, there is no need to transform factors. See
formatRCTDataset for more details.
dataData.frame with format: . Y must be
two levels factor if type is binary. T must be numeric or integer.
screeningLogical, set to FALSE Set to TRUE to use
varimp in trees computation.
varimpCharacter vector of important variables to use in trees computation.
deltaNumeric representing the difference of incidence between treatments.
typeCharacter : binary or continous. Only binary is currently available.
computeDelta()Compute delta value.
getData(interactions = F)Return dataset. If interactions is set to T, return data with treatement interactions
getFormula()Return formula : Y~T+X1+...+Xp. Usefull for cforest function.
getIncidences(rule = NULL)Return incidence table of data if rule set to NULL. Otherwise return incidence for the rule.
getX(interactions = T, trt = NULL)Return predictors (T,X,X*T,X*(1-T)). Or (T,X) if interactions is FALSE. If trt is not NULL, return predictors for T = trt
getXwithInt()Return predictors with interactions. Use VT.object::getX(interactions = T) instead.
getY(trt = NULL)Return outcome. If trt is not NULL, return outcome for T = trt.
switchTreatment()Switch treatment value.
## Not run: # Default use : vt.o <- VT.object$new(data = my.rct.dataset) # Getting data head(vt.o$data) # or getting predictor with interactions vt.o$getX(interactions = T) # or getting X|T = 1 vt.o$getX(trt = 1) # or getting Y|T = 0 vt.o$getY(0) # Print incidences vt.o$getIncidences() ## End(Not run)## Not run: # Default use : vt.o <- VT.object$new(data = my.rct.dataset) # Getting data head(vt.o$data) # or getting predictor with interactions vt.o$getX(interactions = T) # or getting X|T = 1 vt.o$getX(trt = 1) # or getting Y|T = 0 vt.o$getY(0) # Print incidences vt.o$getIncidences() ## End(Not run)
VT.predict generic function
VT.predict(rfor, newdata, type) ## S4 method for signature 'RandomForest,missing,character' VT.predict(rfor, type = "binary") ## S4 method for signature 'RandomForest,data.frame,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'randomForest,missing,character' VT.predict(rfor, type = "binary") ## S4 method for signature 'randomForest,data.frame,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'train,ANY,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'train,missing,character' VT.predict(rfor, type = "binary")VT.predict(rfor, newdata, type) ## S4 method for signature 'RandomForest,missing,character' VT.predict(rfor, type = "binary") ## S4 method for signature 'RandomForest,data.frame,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'randomForest,missing,character' VT.predict(rfor, type = "binary") ## S4 method for signature 'randomForest,data.frame,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'train,ANY,character' VT.predict(rfor, newdata, type = "binary") ## S4 method for signature 'train,missing,character' VT.predict(rfor, type = "binary")
rfor |
random forest model. Can be train, randomForest or RandomForest class. |
newdata |
Newdata to predict by the random forest model. If missing, OOB predictions are returned. |
type |
Must be binary or continous, depending on the outcome. Only binary is really available. |
vector
rfor = RandomForest,newdata = missing,type = character: rfor(RandomForest) newdata (missing) type (character)
rfor = RandomForest,newdata = data.frame,type = character: rfor(RandomForest) newdata (data.frame) type (character)
rfor = randomForest,newdata = missing,type = character: rfor(randomForest) newdata (missing) type (character)
rfor = randomForest,newdata = data.frame,type = character: rfor(randomForest) newdata (data.frame) type (character)
rfor = train,newdata = ANY,type = character: rfor(train) newdata (ANY) type (character)
rfor = train,newdata = missing,type = character: rfor(train) newdata (missing) type (character)
Function which uses VT.tree intern functions. Package
rpart.plot must be loaded. See VT.tree for details.
vt.subgroups(vt.trees, only.leaf = T, only.fav = T, tables = F, verbose = F, compete = F)vt.subgroups(vt.trees, only.leaf = T, only.fav = T, tables = F, verbose = F, compete = F)
vt.trees |
|
only.leaf |
logical to select only leaf of trees. TRUE is default. |
only.fav |
logical select only favorable subgroups (meaning with favorable label of the tree). TRUE is default. |
tables |
set to TRUE if tables of incidence must be shown. FALSE is default. |
verbose |
print infos during computation. FALSE is default. |
compete |
print competitors rules thanks to competitors computation of the tree |
data.frame of rules
data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", TRUE) # inside model : vt.f <- vt.forest("one", vt.o) # use classification tree vt.tr <- vt.tree("class", vt.f, threshold = c(0.01, 0.05)) # show subgroups subgroups <- vt.subgroups(vt.tr) # change options you'll be surprised ! subgroups <- vt.subgroups(vt.tr, verbose = TRUE, tables = TRUE)data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", TRUE) # inside model : vt.f <- vt.forest("one", vt.o) # use classification tree vt.tr <- vt.tree("class", vt.f, threshold = c(0.01, 0.05)) # show subgroups subgroups <- vt.subgroups(vt.tr) # change options you'll be surprised ! subgroups <- vt.subgroups(vt.tr, verbose = TRUE, tables = TRUE)
vt.tree is a wrapper of VT.tree.class and
VT.tree.reg. With parameter tree.type, any of these two class
can be used with its own parameter.
vt.tree(tree.type = "class", vt.difft, sens = ">", threshold = seq(0.5, 0.8, 0.1), screening = NULL, ...)vt.tree(tree.type = "class", vt.difft, sens = ">", threshold = seq(0.5, 0.8, 0.1), screening = NULL, ...)
tree.type |
must be a character. "class" for classification tree, "reg" for regression tree. |
vt.difft |
|
sens |
must be a character c(">","<"). See |
threshold |
must be numeric. It can be a unique value or a vector. If
numeric vector, a list is returned. See |
screening |
must be logical. If TRUE, only varimp variables of VT.object is used to create the tree. |
... |
rpart() function parameters. Can be used for any tree.type. |
See VT.tree, VT.tree.class and
VT.tree.reg classes.
VT.tree or a list of VT.tree depending on threshold
dimension. See examples.
data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T) # inside model : vt.f <- vt.forest("one", vt.o) # use classification tree vt.tr <- vt.tree("class", vt.f, threshold = c(0.01, 0.05)) # return a list class(vt.tr) # access one of the tree tree1 <- vt.tr$tree1 # return infos # vt.tr$tree1$getInfos() # vt.tr$tree1$getRules() # use vt.subgroups tool: subgroups <- vt.subgroups(vt.tr)data(sepsis) vt.o <- vt.data(sepsis, "survival", "THERAPY", T) # inside model : vt.f <- vt.forest("one", vt.o) # use classification tree vt.tr <- vt.tree("class", vt.f, threshold = c(0.01, 0.05)) # return a list class(vt.tr) # access one of the tree tree1 <- vt.tr$tree1 # return infos # vt.tr$tree1$getInfos() # vt.tr$tree1$getRules() # use vt.subgroups tool: subgroups <- vt.subgroups(vt.tr)
An abstract reference class to compute tree
VT.tree.class and VT.tree.reg are children of VT.tree.
VT.tree.class and VT.tree.reg try to find a strong association
between difft (in VT.difft object) and RCT variables.
In VT.tree.reg, a regression tree is computed on difft values.
Then, thanks to the threshold it flags leafs of the tree which
are above the threshold (when sens is ">"). Or it flags leafs
which are below the threshold (when sens = "<").
In VT.tree.class, it first flags difft above or below
(depending on the sens) the given threshold. Then a
classification tree is computed to find which variables explain flagged
difft.
To sum up, VT.tree try to understand which variables are associated
with a big change of difft.
Results are shown with getRules() function. only.leaf parameter
allows to obtain only the leaf of the tree. only.fav parameter
select only favorable nodes. tables shows incidence table of the rule.
verbose allow getRules() to be quiet. And compete show
also rules with maxcompete competitors from the tree.
vt.difftVT.difft object
outcomeoutcome vector from rpart function
thresholdnumeric Threshold for difft calculation (c)
screeningLogical. TRUE if using varimp. Default is VT.object screening field
senscharacter Sens can be ">" (default) or "<". Meaning :
difft > threshold or difft < threshold
namecharacter Names of the tree
treerpart Rpart object to construct the tree
Ahatvector Indicator of beglonging to Ahat
computeNameOfTree(type)return label of response variable of the tree
createCompetitors()Create competitors table
getAhatIncidence()Return Ahat incidence
getAhatQuality()Return Ahat quality
getData()Return data used for tree computation
getIncidences(rule, rr.snd = T)Return incidence of the rule
getInfos()Return infos about tree
getRules(only.leaf = F, only.fav = F, tables = T, verbose = T,
compete = F)Return subgroups discovered by the tree. See details.
run(...)Compute tree with rpart parameters
See VT.tree
run(...)Compute tree with rpart parameters
See VT.tree
run(...)Compute tree with rpart parameters