standscore | R Documentation |
This function evaluates the standardized score process. The process helps for evaluating the goodness of fit of the proportional hazards model and visualizing the shape of time-dependent effects. It is also used in tests of comparison of survival curves.
standscore(formula, data, globstan = TRUE, beta0 = 0)
formula |
A formula object or character string with the time and censoring status separated by "+" on the left hand side and the covariates separated by "+" on the right. For instance, if the time name is "Time", the censoring status is "Status" and the covariates are "Cov1" and "Cov2", the formula is "Time+Status~Cov1+Cov2". |
data |
A data.frame with the data. The censoring status should be 1 for failure and 0 for censoring. No missing data accepted. |
globstan |
With one covariate in the model, globstan has no effect. With several covariates,
|
beta0 |
A vector of parameters to evaluate the process (by default, parameters set to 0). Its length is the number of covariates. Each value corresponds to the regression coefficient for a covariate, in the same order as appearing in formula. |
The program does not handle ties in the data. We suggest to randomly split the ties before using the program.
Score |
A vector or matrix with the value of the standardized score process. Each row corresponds to a failure time, each column to a covariate. |
Sigma |
The matrix used for the standardization of the process. Sigma is the estimator of the variance-covariance matrix between the covariates to the power of - 1 / 2. This value is present only with multiple covariates and globstan = TRUE. |
confbandCOV |
A matrix with the confidence bands of the process for a constant regression effect associated with the covariate named COV. Each row corresponds to a failure time. The first column is the lower band and the second column is the upper band. This value is present with one covariate or with multiple covariates and globstan = TRUE. |
Cecile Chauvel
Chauvel, C, OQuigley, J (2014) Tests for comparing estimated survival functions. Biometrika |
101, 3, 535 – 552. |
Chauvel, C (2014). PhD thesis (in French): Processus empiriques pour l'inférence dans le |
modèle de survie à risques non proportionnels. |
Université Pierre et Marie Curie - Paris VI. |
plotscore
library(survival)
data(ovarian)
#############################################
# Evaluation and plot of the standardized score process at parameter beta0 = 0
score1=standscore(futime+fustat~age+rx,data=ovarian)
plotscore(score1,printCB=TRUE)
#############################################
# Evaluation of the standardized score process at parameter
# beta0 = maximum partial likelihood estimator of beta in the Cox model
beta=coxph(Surv(futime,fustat)~age+rx,data=ovarian)$coeff
score2=standscore(futime+fustat~age+rx,data=ovarian,beta0=beta)
# Separated plots for each regression effect
par(mfrow=c(1,2))
plotscore(score2,printCB=TRUE,component.num=1,main="age")
plotscore(score2,printCB=TRUE,component.num=2,main="rx")
#############################################
# Evaluation and plot of the standardized score process at parameter beta0 = 0
# without global standardization
fo="futime+fustat~age+rx"
score3=standscore(fo,data=ovarian,globstan=FALSE)
plotscore(score3)
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