Thanks for your help!
2009/8/2 David Winsemius <dwinsem...@comcast.net>
> Here's what I would do. Let's assume that you are presenting the results of
> the example on the cph help page. I agree with you that the results should
> be presented on the hazard ratio scale. The Design package provides
> appropriate plotting tools for creation of publication quality graphics. in
> your example I would issue the following command:
> plot(f, age=NA, sex= NA, #age will be on the x axis and separate plots
> will be done for male and female
> col= c("red", "blue"), # fortunately Frank constructed
> plot.Design, so that both the main effects plots and the CI plots get
> colored
> fun = exp, # this results in the log hazard
> effects on the y axis being transformed to the hazard ratio scale
> ylab = "Hazard Ratios") # and this adds a meaningful label fo
> the y axis
>
> An alternative plot would be to suppress the CI lines which might be more
> effective in getting across the message:
>
> plot(f, age=NA, sex= NA, col= c("red", "blue"), fun = exp, ylab = "Hazard
> Ratios", conf.int=FALSE)
>
> Given that the model forces the male-female curves to be equidistant on the
> log hazards scale, I would also examine the possibility that they were in
> "truth" different by creating an interaction model and testing for
> difference between that model and the more simplistic model. In this case it
> is no surprise that the statistical test fails to support such a model:
>
> f2 <- cph(Srv ~ rcs(age,4)* sex, x=TRUE, y=TRUE)
>
> anova(f)
> anova*f2)
>
> Which mean I would not get the challenge of explaining to my audience the
> notion of different forms of the age effect for men and women. You would
> explain in your paper that the model shows that males are on
> average exp(-0.6445) = 0.5249 times as likely to experience the event of
> interest. Ths is not at all typical for ordinary humans and this would
> require careful consideration and review of the experimental situation. The
> hazard ratios at each age relative to a person of average age are displayed
> on the graphic. They show a plateau in mortality at young ages (which is
> typical of free-range humans) but that risk then doubles each decade between
> ages 45 and 60 but the rise tapers off somewhat at the extremes of age.
> Those features are typical for human mortality and would not require as much
> commentary.
>
> --
> David
>
> On Aug 1, 2009, at 8:53 PM, zhu yao wrote:
>
> 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 <dwinsem...@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
>>
>>
>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
>
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