On Jul 31, 2009, at 11:24 PM, zhu yao wrote:
Could someone explain the summary(cph.object)?
The example is in the help file of cph.
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
This is process for setting the range for the display of effects in Design
regression objects. See:
?datadist
"q.effect
set of two quantiles for computing the range of continuous variables to use
in estimating regression effects. Defaults are c(.25,.75), which yields
inter-quartile-range odds ratios, etc."
?summary.Design
#---
" By default, inter-quartile range effects (odds ratios, hazards ratios,
etc.) are printed for continuous factors, ... "
#---
"Value
For summary.Design, a matrix of class summary.Design with rows
corresponding to factors in the model and columns containing the low and
high values for the effects, the range for the effects, the effect point
estimates (difference in predicted values for high and low factor values),
the standard error of this effect estimate, and the lower and upper
confidence limits."
#---
Srv <- Surv(dt,e)
f <- cph(Srv ~ rcs(age,4) + sex, x=TRUE, y=TRUE)
summary(f)
Effects Response : Srv
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
age 40.872 57.385 16.513 1.21 0.21 0.80 1.62
Hazard Ratio 40.872 57.385 16.513 3.35 NA 2.22 5.06
In this case with a 4 df regression spline, you need to look at the
"effect" across the range of the variable. You ought to plot the age effect
and examine anova(f) ). In the untransformed situation the plot is on the
log hazards scale for cph. So the effect for age in this case should be the
difference in log hazard at ages 40.872 and 57.385. SE is the standard error
of that estimate and the Upper and Lower numbers are the confidence bounds
on the effect estimate. The Hazard Ratio row gives you exponentiated
results, so a difference in log hazards becomes a hazard ratio. {exp(1.21) =
3.35}
sex - Female:Male 2.000 1.000 NA 0.64 0.15 0.35 0.94
Hazard Ratio 2.000 1.000 NA 1.91 NA 1.42 2.55
Wat's the meaning of Effect, S.E. Lower, Upper?
You probably ought to read a bit more basic material. If you are asking
this question, Harrell's "Regression Modeling Strategies" might be over you
head, but it would probably be a good investment anyway. Venables and
Ripley's "Modern Applied Statistics" has a chapter on survival analysis.
Also consider Kalbfliesch and Prentice "Statistical Analysis of Failure Time
Data". I'm sure there are others; those are the ones I have on my shelf.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
