Re: [R] about the summary(cph.object)
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
FactorLowHigh 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
Re: [R] about the summary(cph.object)
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
FactorLowHigh 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
Re: [R] about the summary(cph.object)
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
FactorLowHigh 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
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] about the summary(cph.object)
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
FactorLowHigh 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
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] about the summary(cph.object)
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?
David explained this. Nonlinearity in age does not complicate the
explanation. The estimate is the estimate of the ratio of hazard rate
at the upper quartile of age compared to the hazard ratio at the lower
quartile, with the ages corresponding to these 2 points shown in the output.
The output of f is not very useful for publication. The output of
summary, Function, and latex are.
Frank
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
FactorLowHigh 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
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] about the summary(cph.object)
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')
Srv - Surv(dt,e)
f - cph(Srv ~ rcs(age,4) + sex, x=TRUE, y=TRUE)
summary(f)
Effects Response : Srv
FactorLowHigh 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
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?
Thanks
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
