zhu yao wrote:
Thanks for your reply.
Actually, I'm confused about the results in the article "Postoperative
nomogram for survival of patients with retroperitoneal sarcoma treated
with curative intent"
http://annonc.oxfordjournals.org/cgi/content/abstract/mdp298v1
It stated as:
nomogram model
The Cox model was used as the basis for the nomogram
(Table 2). Figure 2 depicts the final nomogram and portrays the
association between each variable and survival based on the
scoring system derived from this analysis. The concordance
index (discrimination) after internal validation with 200
bootstrapping resamples was 0.73 (95% CI 0.71–0.75).
Similarly, Figure 3 illustrates the calibration of the nomogram
before and after internal validation with bootstrapping samples.
Calibration was excellent with observed outcomes always
within 95% CI of the predicted survival probability.
Figure 3 is provided by the Design package without modification. As I
stated before it does provide those CIs for survival models. I guess
that the CI for the c-index was obtained without bootstrap validation
using the Hmisc package's rcorr.cens function (and Dxy=2*(C-.5)) or by
using an approximate bootstrap analysis they programmed.
Note that in the abstract the authors wrongly used the confidence
intervals in Fig 3 to conclude excellent validation of the model. Their
conclusion can arise from just having large confidence intervals.
Frank
Figure 3 is
http://i3.6.cn/cvbnm/a9/c8/8b/c01aad248a0b4ae6ef677600614bd4fa.jpg
2009/7/26 Frank E Harrell Jr <f.harr...@vanderbilt.edu
<mailto:f.harr...@vanderbilt.edu>>
>
> zhu yao wrote:
>>
>> Dear experts:
>>
>> I am a newbie to R. Recently, I try to make prediction models with R
and the
>> Design library.
>> I have read Prof. Harrell's excellent book. But I did not quite
understand.
>> I have two problems about the validation and calibration of prediction
>> models:
>> 1. Can someone explain the results outputted by the validate()
function? How
>> to get 95% of c-value of validate?
>
> validate does not provide that confidence interval, unfortunately.
>
>> 2. How to add 95% ci in the calibration plot?
>
> That is not provided except for survival models.
>
> Next time please include your code so we can see what model you are
using.
>
> Thanks
> Frank
>
>>
>> Yao Zhu
>> Department of Urology
>> Fudan University Shanghai Cancer Center
>>
>> [[alternative HTML version deleted]]
>>
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>>
>
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