On Tue, 19 Jun 2007, John Logsdon wrote:
> In survreg() the predictor is log(characteristic life) for Weibull (=
> exponential when scale=1) - ie the 63.2%ile. For the others the predictor is
> log(median).
>
> This causes problems when comparing predictions and a better way IMHO is to
> correct the Weibull prediction by a factor (log(2))^(1/scale). This is only
> a simple multiple unless the shape parameter is also being modelled, when a
> completely different solution may arise. Such heterogeneity modelling cannot
> of course be done within survreg().
Except, of course, for a discrete predictor of heterogeneity, using
strata().
-thomas
> On Monday 18 June 2007 22:56:54 Frank E Harrell Jr wrote:
>> sj wrote:
>>> I am using psm to model some parametric survival data, the data is for
>>> length of stay in an emergency department. There are several ways a
>>> patient's stay in the emergency department can end (discharge, admit,
>>> etc..) so I am looking at modeling the effects of several covariates on
>>> the various outcomes. Initially I am trying to fit a survival model for
>>> each type of outcome using the psm function in the design package, i.e.,
>>> all patients who's visits come to an end due to any event other than
>>> the event of interest are considered to be censored. Being new to the
>>> psm and survreg packages (and to parametric survival modeling) I am not
>>> entirely sure how to interpret the coefficient values that psm returns. I
>>> have included the following code to illustrate code similar to what I am
>>> using on my data. I suppose that the coefficients are somehow rescaled ,
>>> but I am not sure how to return them to the original scale and make sense
>>> out of the coefficients, e.g., estimate the the effect of higher acuity
>>> on time to event in minutes. Any explanation or direction on how to
>>> interpret the coefficient values would be greatly appreciated.
>>>
>>> this is from the documentation for survreg.object.
>>> coefficientsthe coefficients of the linear.predictors, which multiply the
>>> columns of the model matrix. It does not include the estimate of error
>>> (sigma). The names of the coefficients are the names of the
>>> single-degree-of-freedom effects (the columns of the model matrix). If
>>> the model is over-determined there will be missing values in the
>>> coefficients corresponding to non-estimable coefficients.
>>>
>>> code:
>>> LOS <- sort(rweibull(1000,1.4,108))
>>> AGE <- sort(rnorm(1000,41,12))
>>> ACUITY <- sort(rep(1:5,200))
>>> EVENT <- sample(x=c(0,1),replace=TRUE,1000)
>>> psm(Surv(LOS,EVENT)~AGE+as.factor(ACUITY),dist='weibull')
>>>
>>> output:
>>>
>>> psm(formula = Surv(LOS, CENS) ~ AGE + as.factor(ACUITY), dist =
>>> "weibull")
>>>
>>> Obs Events Model L.R. d.f. P R2
>>> 1000 513 2387.62 5 0 0.91
>>>
>>> Value Std. Error z p
>>> (Intercept) 1.1055 0.04425 24.98 8.92e-138
>>> AGE 0.0772 0.00152 50.93 0.00e+00
>>> ACUITY=2 0.0944 0.01357 6.96 3.39e-12
>>> ACUITY=3 0.1752 0.02111 8.30 1.03e-16
>>> ACUITY=4 0.1391 0.02722 5.11 3.18e-07
>>> ACUITY=5 -0.0544 0.03789 -1.43 1.51e-01
>>> Log(scale) -2.7287 0.03780 -72.18 0.00e+00
>>>
>>> Scale= 0.0653
>>>
>>> best,
>>>
>>> Spencer
>>
>> I have a case study using psm (survreg wrapper) in my book. Briefly,
>> coefficients are on the log median survival time scale.
>>
>> Frank
>
>
>
> --
> Best wishes
>
> John
>
> John Logsdon "Try to make things as simple
> Quantex Research Ltd, Manchester UK as possible but not simpler"
> [EMAIL PROTECTED] [EMAIL PROTECTED]
> +44(0)161 445 4951/G:+44(0)7717758675 www.quantex-research.com
>
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>
Thomas Lumley Assoc. Professor, Biostatistics
[EMAIL PROTECTED] University of Washington, Seattle
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