Description Usage Arguments Value Acknowledgments Author(s) References See Also Examples
Fits a Proportional Hazards TimeToEvent Regression Model saturated with first order terms. Computes pvalues of significance of regression coefficients of main effects in a CoxPH model
1 2  cph.main(X,
main.term)

X 

main.term 

List
of 2 fields:
raw 
Raw pvalue covariates importances significance 
fdr 
FDRadjusted pvalue of covariates importances significance 
This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. We are thankful to Ms. Janet Schollenberger, Senior Project Coordinator, CAMACS, as well as Dr. Jeremy J. Martinson, Sudhir Penugonda, Shehnaz K. Hussain, Jay H. Bream, and Priya Duggal, for providing us the data related to the samples analyzed in the present study. Data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS) at (http://www.statepi.jhsph.edu/macs/macs.html) with centers at Baltimore, Chicago, Los Angeles, Pittsburgh, and the Data Coordinating Center: The Johns Hopkins University Bloomberg School of Public Health. The MACS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional cofunding from the National Cancer Institute (NCI), the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute on Deafness and Communication Disorders (NIDCD). MACS data collection is also supported by Johns Hopkins University CTSA. This study was supported by two grants from the National Institute of Health: NIDCR P01DE019759 (Aaron Weinberg, Peter Zimmerman, Richard J. Jurevic, Mark Chance) and NCI R01CA163739 (Hemant Ishwaran). The work was also partly supported by the National Science Foundation grant DMS 1148991 (Hemant Ishwaran) and the Center for AIDS Research grant P30AI036219 (Mark Chance).
JeanEudes Dazard <[email protected]>
Maintainer: JeanEudes Dazard <[email protected]>
Dazard JE., Ishwaran H., Mehlotra R.K., Weinberg A. and Zimmerman P.A. "Ensemble Survival Tree Models to Reveal Variable Interactions in Association with TimetoEvents Outcomes" Submitted (2017).
Ishwaran, H. and Kogalur, U.B. "Random Survival Forests for R. R News, 7(2), 2531, (2007).
Ishwaran, H. and Kogalur, U.B.
"Contributed R Package randomForestSRC
: Random Forests for Survival, Regression and Classification (RFSRC)"
CRAN (2013).
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 41 42 43 44 45 46 47 48 49 50  ## Not run:
#===================================================
# Loading the library and its dependencies
#===================================================
library("IRSF")
#==========================================================================================#
# Continuous case:
# All variables xj, j in {1,...,p}, are iid from a multivariate uniform distribution
# with parmeters a=1, b=5, i.e. on [1, 5].
# rho = 0.50
# Regression model: X1 + X5
#==========================================================================================#
seed < 1234567
set.seed(seed)
n < 200
p < 5
x < matrix(data=runif(n=n*p, min=1, max=5),
nrow=n, ncol=p, byrow=FALSE,
dimnames=list(1:n, paste("X", 1:p, sep="")))
beta < c(1,0,0,0,1)
covar < x
eta < covar
seed < 1234567
set.seed(seed)
lambda0 < 1
lambda < lambda0 * exp(eta  mean(eta)) # hazards function
tt < rexp(n=n, rate=lambda) # true (uncensored) event times
tc < runif(n=n, min=0, max=1.50) # true (censored) event times
stime < pmin(tt, tc) # observed event times
status < 1 * (tt <= tc) # observed event indicator
X < data.frame(stime, status, x)
main.mdms < rsf.main(X=X,
ntree=1000,
method="mdms",
splitrule="logrank",
importance="random",
B=10,
ci=90,
parallel=FALSE,
conf=NULL,
verbose=TRUE,
seed=seed)
main.cph < cph.main(X=X,
main.term=rownames(main.mdms))
## End(Not run)

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