Thanks for your thoughtful response. See below:
Steven Samuels wrote:
Jeph,
You don't need medians at all, just estimated 30 day readmission rates.
I'm not up on this literature, but you might try the following:
1. Fit a survival model with hospital random effects, using -streg-,
-stcox-, or -gllamm- with the -frailty- options to define the
distributions of the random effects. You can probably improve your
predictive model if you add hospital factors as predictors.
Since we are creating hospital scorecards, we're not allowed to
include hospital factors.
2. Rank on the estimated hospital effects. (If you have hospital level
factors in your model, add their contribution to the random effect).
You can convert the model parameters to estimates of the probabilities
of 30 day readmission.
I can rank on the hospital effects, but how do I convert the model
parameters to estimates of the probability of 30-day readmission?
I would start with -streg- and -stcox- because you have already -stset-
your data. Try different distributions for survival time and (for
-streg-) different frailty distributions. You don't have to believe
that these distributions fit beyond 30 days. Rank the estimated hospital
effects, adjusted for patient covariates (log survival time scale or log
hazard scale). Check on the agreement of the rankings from the different
specifications. You may be able to use BIC or some other criterion to
choose a "best" model.
I did this initially, comparing LLs, AICs, and plots of Cox-Snell
residuals. The log-normal model carried the day.
If so, create predictions of the 30 day
readmission rates and CI's from this model.
This is the part I'm missing. How do I get predicted 30-day rates from
this model?
thanks,
Jeph
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
