The `pact`

package implements a prediction-based approach to the
analysis of data from randomized clinical trials (RCT). Based on clincial response and
covariate data from a RCT comparing a new experimental treatment E versus a control C,
the purpose behind the functions in `pact`

is to develop and internally validate
a model that can identify subjects likely to benefit from E rather than C. Currently,
'survival' and 'binary' response types are permitted.

Package: pact

Type: Package

Version: 0.5.0

Date: 2016-04-14

Author: Dr. Jyothi Subramanian and Dr. Richard Simon

Maintainer: Jyothi Subramanian <subramanianj01@gmail.com>

License: GPL-3

`pact.fit`

fits a predictive model to data from RCT. Currently, 'survival' and
'binary' response types are supported. Analysis of high dimensional covariate data is supported.
If known and available, a limited number of prognostic covariates can also be specified and
fixed to remain in the predictive model. An object of class 'pact' is returned.
`print`

, `summary`

and `predict`

methods are available for objects of
class 'pact'.
Additionally, the function `pact.cv`

takes as an input the object returned by `pact.fit`

and computes predictive scores for each subject through k-fold cross-validation.
Evaluations of the cross-validated predictions are performed by the function `eval.pact.cv`

.

Finally, the function `overall.analysis`

also takes an object of class 'pact' as input and computes some summary statistics
for the comparison of treatments E and C.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
### Survival response
set.seed(10)
data(prostateCancer)
Y <- prostateCancer[,3:4]
Xf <- prostateCancer[,7:8] ## Prognostic covariates fixed to always be in the model
Xv <- prostateCancer[,c(5:6,9)]
Treatment <- as.factor(prostateCancer[,2])
p <- pact.fit(Y=Y,Xf=Xf,Xv=Xv,Treatment=Treatment,family="cox",varSelect="univar")
print(p)
overall.analysis(p)
cv <- pact.cv(p, nfold=5)
eval.pact.cv(cv, method="continuous", plot.score=TRUE, perm.test=FALSE, nperm=100)
### Binary response
set.seed(10)
data(EORTC10994)
Y <- as.factor(EORTC10994[,4])
## No prognostic covariates (Xf) specified
Xv <- EORTC10994[,c(2,5:7)]
Treatment <- as.factor(EORTC10994[,3])
p <- pact.fit(Y=Y,Xv=Xv,Treatment=Treatment,family="binomial",varSelect="none")
print(p)
overall.analysis(p)
cv <- pact.cv(p, nfold=5)
eval.pact.cv(cv, method="discrete", g=log(1), perm.test=FALSE, nperm=100)
### High dimensional data, survival response
## Not run:
set.seed(10)
data(GSE10846)
Y <- GSE10846[,1:2]
Xv <- GSE10846[,-c(1:3)]
Treatment <- as.factor(GSE10846[,3])
p <- pact.fit(Y=Y,Xv=Xv,Treatment=Treatment,family="cox",varSelect="lasso",penalty.scaling=2)
print(p)
overall.analysis(p)
cv <- pact.cv(p, nfold=5)
eval.pact.cv(cv, method="continuous", plot.score=TRUE, perm.test=FALSE)
## End(Not run)
``` |

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