cph.main: Main effects in a Cox-PH model

Description Usage Arguments Value Acknowledgments Author(s) References See Also Examples

View source: R/IRSF.r

Description

Fits a Proportional Hazards Time-To-Event Regression Model saturated with first order terms. Computes p-values of significance of regression coefficients of main effects in a Cox-PH model

Usage

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    cph.main(X,
             main.term)

Arguments

X

data.frame or numeric matrix of input covariates. Dataset X assumes that: - all variables are in columns - the observed times to event and censoring variables are in the first two columns: "stime": numeric vector of observed times. "status": numeric vector of observed status (censoring) indicator variable. - each variable has a unique name, excluding the word "noise"

main.term

Vector of character string of each individual covariate name.

Value

List of 2 fields:

raw

Raw p-value covariates importances significance

fdr

FDR-adjusted p-value of covariates importances significance

Acknowledgments

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 co-funding 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).

Author(s)

Jean-Eudes Dazard <[email protected]>

Maintainer: Jean-Eudes Dazard <[email protected]>

References

See Also

randomForestSRC

Examples

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   ## 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)

jedazard/IRSF documentation built on Oct. 19, 2017, 11:49 p.m.