[R] psm/survreg coefficient values ?
John Logsdon
j.logsdon at quantex-research.com
Tue Jun 19 11:17:48 CEST 2007
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().
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"
j.logsdon at quantex-research.com a.einstein at relativity.org
+44(0)161 445 4951/G:+44(0)7717758675 www.quantex-research.com
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