Fast Cox PH regression — phreg • mets

Fast Cox PH regression Robust variance is default variance with the summary.

Usage

phreg(formula, data, offset = NULL, weights = NULL, ...)

Arguments

formula: formula with 'Surv' outcome (see coxph)
data: data frame
offset: offsets for Cox model
weights: weights for Cox score equations
...: Additional arguments to lower level funtions

Details

influence functions (iid) will follow numerical order of given cluster variable so ordering after $id will give iid in order of data-set.

Author

Klaus K. Holst, Thomas Scheike

Examples

library(mets)
data(TRACE)
dcut(TRACE) <- ~.
out1 <- phreg(Surv(time,status==9)~wmi+age+strata(vf,chf)+cluster(id),data=TRACE)
summary(out1)
#> 
#>     n events
#>  1878    958
#> coeffients:
#>       Estimate       S.E.    dU^-1/2 P-value
#> wmi -0.8470690  0.0871082  0.0864370       0
#> age  0.0563491  0.0037111  0.0036386       0
#> 
#> exp(coeffients):
#>     Estimate    2.5%  97.5%
#> wmi  0.42867 0.36139 0.5085
#> age  1.05797 1.05030 1.0657
#> 

par(mfrow=c(1,2))
plot(out1)

## computing robust variance for baseline
rob1 <- robust.phreg(out1)
plot(rob1,se=TRUE,robust=TRUE)


## iid decomposition, with scaled influence functions
## for regression parameters
head(iid(out1))
#>              wmi           age
#> 3   2.315573e-04  8.113514e-05
#> 7  -1.175364e-03 -2.901628e-05
#> 13  1.826339e-03  1.670430e-04
#> 15 -3.739823e-05  2.673793e-05
#> 17 -2.997039e-03  5.371497e-05
#> 22 -9.146118e-04 -9.342247e-06
## making iid decomposition of baseline at a specific time-point
Aiiid <- iid(out1,time=30)
head(Aiiid)
#>          strata0       strata1       strata2       strata3
#> 3  -1.894877e-04 -4.120570e-04 -2.659857e-04 -7.350908e-04
#> 7   1.812767e-04  2.367745e-04  1.678711e-04  8.774722e-04
#> 13 -6.837818e-04 -1.063129e-03 -6.374803e-04 -1.717426e-03
#> 15 -8.543458e-05 -9.149095e-05 -8.008394e-05 -2.250601e-04
#> 17  4.118052e-05  3.243790e-05  3.366936e-05 -1.046447e-05
#> 22  5.011669e-05  1.201129e-04  8.975026e-05  2.188617e-04
## both iid decompositions
dd <- iidBaseline(out1,time=30)
head(dd$beta.iid)
#>             [,1]          [,2]
#> 3   2.315573e-04  8.113514e-05
#> 7  -1.175364e-03 -2.901628e-05
#> 13  1.826339e-03  1.670430e-04
#> 15 -3.739823e-05  2.673793e-05
#> 17 -2.997039e-03  5.371497e-05
#> 22 -9.146118e-04 -9.342247e-06
head(dd$base.iid)
#>          strata0       strata1       strata2       strata3
#> 3  -1.894877e-04 -4.120570e-04 -2.659857e-04 -7.350908e-04
#> 7   1.812767e-04  2.367745e-04  1.678711e-04  8.774722e-04
#> 13 -6.837818e-04 -1.063129e-03 -6.374803e-04 -1.717426e-03
#> 15 -8.543458e-05 -9.149095e-05 -8.008394e-05 -2.250601e-04
#> 17  4.118052e-05  3.243790e-05  3.366936e-05 -1.046447e-05
#> 22  5.011669e-05  1.201129e-04  8.975026e-05  2.188617e-04

outs <- phreg(Surv(time,status==9)~strata(vf,wmicat.4)+cluster(id),data=TRACE)
summary(outs)
#> 
#>     n events
#>  1878    958
#> 
par(mfrow=c(1,2))
plot(outs)