Fast Cox PH regression Robust variance is default variance with the summary.
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.
Examples
data(TRACE)
dcut(TRACE) <- ~.
out1 <- phreg(Surv(time,status==9)~vf+chf+strata(wmicat.4),data=TRACE)
out2 <- phreg(Event(time,status)~vf+chf+strata(wmicat.4),data=TRACE)
## tracesim <- timereg::sim.cox(out1,1000)
## sout1 <- phreg(Surv(time,status==1)~vf+chf+strata(wmicat.4),data=tracesim)
## robust standard errors default
summary(out1)
#>
#> n events
#> 1878 958
#> coeffients:
#> Estimate S.E. dU^-1/2 P-value
#> vf 0.452306 0.136473 0.111038 9e-04
#> chf 0.931822 0.074226 0.074650 0e+00
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> vf 1.5719 1.2030 2.0540
#> chf 2.5391 2.1954 2.9367
#>
#>
out1 <- phreg(Surv(time,status!=0)~vf+chf+strata(wmicat.4),data=TRACE)
summary(out2)
#>
#> n events
#> 1878 970
#> coeffients:
#> Estimate S.E. dU^-1/2 P-value
#> vf 0.455201 0.135555 0.110436 8e-04
#> chf 0.903063 0.073354 0.073780 0e+00
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> vf 1.5765 1.2087 2.0563
#> chf 2.4671 2.1368 2.8486
#>
#>
par(mfrow=c(1,2))
bplot(out1)
## bplot(sout1,se=TRUE)
## computing robust variance for baseline
rob1 <- robust.phreg(out1)
bplot(rob1,se=TRUE,robust=TRUE)
## making iid decomposition of regression parameters
betaiiid <- lava::iid(out1)
## making iid decomposition of baseline at a specific time-point
Aiiid <- mets:::IIDbaseline.phreg(out1,time=30)