Thx for your reply.
In this example, age was transformed with rcs. So the output was different
between f and summary(f).
If I need to publicate the results, how do I explation the hazard ratio of
age?

2009/8/1 David Winsemius <dwinsemius@comcast.net>

>
> 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
>
>

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