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