phreg: Fast Cox PH regression

In mets: Analysis of Multivariate Event Times

Description

Fast Cox PH regression

Usage

phreg(formula, data, ...)

Arguments

formula	formula with 'Surv' outcome (see coxph)
data	data frame
...	Additional arguments to lower level funtions

Author(s)

Klaus K. Holst

Examples

simcox <- function(n=1000, seed=1, beta=c(1,1), entry=TRUE) {
  if (!is.null(seed))
    set.seed(seed)
  library(lava)
  m <- lvm()
  regression(m,T~X1+X2) <- beta
  distribution(m,~T+C) <- coxWeibull.lvm(scale=1/100)
  distribution(m,~entry) <- coxWeibull.lvm(scale=1/10)
  m <- eventTime(m,time~min(T,C=0),"status")
  d <- sim(m,n);
  if (!entry) d$entry <- 0
  else d <- subset(d, time>entry,select=-c(T,C))
  return(d)
}
## Not run: 
n <- 1e3;
d <- mets:::simCox(n); d$id <- seq(nrow(d)); d$group <- factor(rbinom(nrow(d),1,0.5))

(m1 <- phreg(Surv(entry,time,status)~X1+X2,data=d))
(m2 <- coxph(Surv(entry,time,status)~X1+X2+cluster(id),data=d))
(coef(m3 <-cox.aalen(Surv(entry,time,status)~prop(X1)+prop(X2),data=d)))


(m1b <- phreg(Surv(entry,time,status)~X1+X2+strata(group),data=d))
(m2b <- coxph(Surv(entry,time,status)~X1+X2+cluster(id)+strata(group),data=d))
(coef(m3b <-cox.aalen(Surv(entry,time,status)~-1+group+prop(X1)+prop(X2),data=d)))

m <- phreg(Surv(entry,time,status)~X1*X2+strata(group)+cluster(id),data=d)
m
plot(m,ylim=c(0,1))

## End(Not run)

Example output

Loading required package: timereg
Loading required package: survival
Loading required package: lava
mets version 1.2.8.1
Call:
phreg(formula = Surv(entry, time, status) ~ X1 + X2, data = d)

   n events
 766    345

 766 clusters
log-coeffients:
   Estimate     S.E.  dU^-1/2 P-value
X1 1.012792 0.065009 0.070213       0
X2 0.911190 0.066907 0.063496       0

exp(coeffients):
     Estimate Std.Err    2.5%   97.5% P-value
[X1]  2.75328 0.17899 2.40247 3.10409       0
[X2]  2.48728 0.16642 2.16111 2.81345       0

Call:
coxph(formula = Surv(entry, time, status) ~ X1 + X2, data = d, 
    cluster = id)

      coef exp(coef) se(coef) robust se     z      p
X1 1.01279   2.75328  0.07021   0.06501 15.58 <2e-16
X2 0.91119   2.48728  0.06350   0.06691 13.62 <2e-16

Likelihood ratio test=360.9  on 2 df, p=< 2.2e-16
n= 766, number of events= 345 
         Coef.     SE Robust SE D2log(L)^-1    z P-val lower2.5% upper97.5%
prop(X1) 1.010 0.0713    0.0650      0.0702 15.6     0     0.870       1.15
prop(X2) 0.911 0.0643    0.0669      0.0635 13.6     0     0.785       1.04
Call:
phreg(formula = Surv(entry, time, status) ~ X1 + X2 + strata(group), 
    data = d)

   n events
 766    345

 766 clusters
log-coeffients:
   Estimate     S.E.  dU^-1/2 P-value
X1 1.014093 0.065559 0.070452       0
X2 0.912926 0.065355 0.063851       0

exp(coeffients):
     Estimate Std.Err    2.5%   97.5% P-value
[X1]  2.75686 0.18074 2.40262 3.11110       0
[X2]  2.49160 0.16284 2.17244 2.81076       0

Call:
coxph(formula = Surv(entry, time, status) ~ X1 + X2 + strata(group), 
    data = d, cluster = id)

      coef exp(coef) se(coef) robust se     z      p
X1 1.01409   2.75686  0.07045   0.06556 15.47 <2e-16
X2 0.91293   2.49160  0.06385   0.06535 13.97 <2e-16

Likelihood ratio test=359.9  on 2 df, p=< 2.2e-16
n= 766, number of events= 345 
         Coef.     SE Robust SE D2log(L)^-1    z P-val lower2.5% upper97.5%
prop(X1) 1.010 0.0709    0.0656      0.0705 15.5     0     0.871       1.15
prop(X2) 0.913 0.0644    0.0654      0.0639 14.0     0     0.787       1.04
Call:
phreg(formula = Surv(entry, time, status) ~ X1 * X2 + strata(group) + 
    cluster(id), data = d)

   n events
 766    345

 766 clusters
log-coeffients:
       Estimate      S.E.   dU^-1/2 P-value
X1     1.018187  0.065551  0.070752  0.0000
X2     0.920881  0.068055  0.065246  0.0000
X1:X2 -0.042774  0.066416  0.069018  0.5196

exp(coeffients):
        Estimate  Std.Err     2.5%    97.5% P-value
[X1]    2.768171 0.181457 2.412522 3.123820  0.0000
[X2]    2.511502 0.170919 2.176506 2.846498  0.0000
[X1:X2] 0.958128 0.063635 0.833405 1.082850  0.5105

mets documentation built on May 2, 2019, 4:43 p.m.