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
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