goftte (version 1.0.5)

prop.crr: Checking proportional subdistribution hazard assumption for Fine & Gray model

Description

Diagnostics for the proportional subdistribution hazards of specific covariate(s) of the Fine & Gray model. Similarly to Li (2015), the limiting null distribution of the score process is approximated by extending Lin's method (1993) to take into account competing events. An adaptation of Liu's approximation method (2008) for Fine & Gray model is also provided. P-values are derived for KS, CvM and AD statistics.

Usage

# S3 method for crr
prop(model, fstatus, ftime, cov1, cencode=0, failcode=1, variable=NULL, 
  type.test=c("Liu"), R=1000, plots=min(R,50), seed=NULL,...)

Arguments

model

Model object (crr).

fstatus

Vector corresponding to the failures of the n patients.

ftime

Vector corresponding to the failure times.

cov1

matrix whose columns consist in the components of the p covariates.

cencode

Censoring code.

failcode

Interest event code. All the failures differing from cencode and failcode are considered as competing events.

type.test

Type of approximation. Values are "Lin" or "Liu". Default is "Liu".

R

Generation number used for Monte-Carlo simulations. This is also an output argument.

plots

Realizations number of Monte-Carlo simulations to save for use in the plot-routine.

seed

Random seed.

variable

Vector corresponding to the labels of each covariate. This is also an output argument.

...

additional arguments.

Value

Returns an object of class 'scproc'. The main items of this object are :

obs

m x p matrix of unique times. m is the length of unique times.

W

The process \(U(\widehat{\beta},t)\) adapted to Fine & Gray model.

What

The simulated limiting processes for the R-plots first Monte-Carlo realizations.

sdw

Standard error over time of What.

cvalues

R x p matrix whose components are the supremum of the standardized What process for each Monte-Carlo realization . The quantiles of this output argument are used to calculate the prediction bands in the plot-routine.

KS

Vector of the p rejection probabilities using KS type statistic.

CvM

Vector of the p rejection probabilities using CvM type statistic.

AD

Vector of the p rejection probabilities using AD type statistic.

References

Li J, Scheike TH and Zhang MJ (2015). Checking Fine & Gray subditribution hazards model with cumulative sums of residuals. Lifetime Data Analysis, 21(2), 197-217.

Lin DY, Wei JL and Ying Z (1993).Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika, 80(3), 557-572.

Liu M, Lu W and Shao (2008). A Monte Carlo approach for change-point detection in the Cox proportional hazards model. Statistics in Medecine, 27(19), 3894-3909.

Examples

# NOT RUN {
require(cmprsk)

#Simulating survival data with competing events
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2,200,replace=TRUE)
cov <- matrix(runif(200),nrow=200)

# Fine & Gray regression
fit.crr <- crr(ftime,fstatus,cov)

#Checking the proportional subdistribution hazards assumption
prop(model=fit.crr, ftime=ftime,fstatus=fstatus,cov1=cov)
# }