[R] psm/survreg coefficient values ?

[Frank E Harrell Jr]

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


-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University