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 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.