Cumulative residuals after model matrix for Cox PH regression p-values based on Lin, Wei, Ying resampling.
Usage
gofM.phreg(
formula,
data,
offset = NULL,
weights = NULL,
modelmatrix = NULL,
n.sim = 1000,
silent = 1,
...
)Arguments
- formula
formula for cox regression
- data
data for model
- offset
offset
- weights
weights
- modelmatrix
matrix for cumulating residuals
- n.sim
number of simulations for score processes
- silent
to keep it absolutely silent, otherwise timing estimate will be prduced for longer jobs.
- ...
Additional arguments to lower level funtions
Details
That is, computes $$ U(t) = \int_0^t M^t d \hat M $$ and resamples its asymptotic distribution.
This will show if the residuals are consistent with the model. Typically, M will be a design matrix for the continous covariates that gives for example the quartiles, and then the plot will show if for the different quartiles of the covariate the risk prediction is consistent over time (time x covariate interaction).
Examples
library(mets)
data(TRACE)
set.seed(1)
TRACEsam <- blocksample(TRACE,idvar="id",replace=FALSE,100)
dcut(TRACEsam) <- ~.
mm <- model.matrix(~-1+factor(wmicat.4),data=TRACEsam)
m1 <- gofM.phreg(Surv(time,status==9)~vf+chf+wmi,data=TRACEsam,modelmatrix=mm)
summary(m1)
#> Cumulative residuals versus modelmatrix :
#> Sup_t |U(t)| pval
#> factor(wmicat.4)[0.4,1.1] 5.788752 0.021
#> factor(wmicat.4)(1.1,1.4] 2.633143 0.591
#> factor(wmicat.4)(1.4,1.72] 5.657370 0.038
#> factor(wmicat.4)(1.72,2] 1.227485 0.948
#>
#> Cumulative score process versus covariates (discrete z via model.matrix):
#> Sup_z |U(tau,z)| pval
#> matrixZ 1.801626 0.812
if (interactive()) {
par(mfrow=c(2,2))
plot(m1)
}
m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam,modelmatrix=mm)
summary(m1)
#> Cumulative residuals versus modelmatrix :
#> Sup_t |U(t)| pval
#> factor(wmicat.4)[0.4,1.1] 6.049754 0.022
#> factor(wmicat.4)(1.1,1.4] 2.211031 0.757
#> factor(wmicat.4)(1.4,1.72] 5.040704 0.085
#> factor(wmicat.4)(1.72,2] 1.482109 0.890
#>
#> Cumulative score process versus covariates (discrete z via model.matrix):
#> Sup_z |U(tau,z)| pval
#> matrixZ 1.228032 0.934
## cumulative sums in covariates, via design matrix mm
mm <- cumContr(TRACEsam$wmi,breaks=10,equi=TRUE)
m1 <- gofM.phreg(Surv(time,status==9)~strata(vf)+chf+wmi,data=TRACEsam,
modelmatrix=mm,silent=0)
#> Cumulative score process test for modelmatrix:
#> Sup_t |U(t)| pval
#> <=0.56 0.84 0.35
#> <=0.72 1.93 0.39
#> <=0.88 5.06 0.04
#> <=1.04 2.63 0.58
#> <=1.2 4.27 0.17
#> <=1.36 4.04 0.17
#> <=1.52 3.20 0.29
#> <=1.68 1.83 0.81
#> <=1.84 1.25 0.82
#> <=2 0.00 1.00
summary(m1)
#> Cumulative residuals versus modelmatrix :
#> Sup_t |U(t)| pval
#> <=0.56 0.8423856 0.352
#> <=0.72 1.9258405 0.394
#> <=0.88 5.0552596 0.037
#> <=1.04 2.6334412 0.579
#> <=1.2 4.2747875 0.174
#> <=1.36 4.0393945 0.169
#> <=1.52 3.2004455 0.292
#> <=1.68 1.8330704 0.810
#> <=1.84 1.2509429 0.823
#> <=2 0.0000000 1.000
#>
#> Cumulative score process versus covariates (discrete z via model.matrix):
#> Sup_z |U(tau,z)| pval
#> matrixZ 3.73439 0.283