Re: [R] psm/survreg coefficient values ? 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@quantex-research.com              a.einstein@relativity.org +44(0)161 445 4951/G:+44(0)7717758675       www.quantex-research.com ______________________________________________ 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.