Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Bootstrapping and predicted probabilities

From   Jeph Herrin <>
Subject   Re: st: Bootstrapping and predicted probabilities
Date   Thu, 11 Apr 2013 13:32:20 -0400

On further thought, while you can do the bootstrapping described below or they reply by Steve Samuels, this doesn't really make sense. bootstrapping is used to for calculating group level statistics - estimating the variance of parameters of the population. If you had multiple observations per patient, then it would make sense to bootstrap those observations to get an estimate of the variance of the predicted probability for that patient, but what you propose to do is to get the predicted probability for a single observation multiple times. It is strange.

Since it seems you want hospital level estimates, you should instead, for each bootstrap sample, calculate the quantity predicted/expected for each hospital, and collate those for each hospital. I think you'd want to stratify on hospital when you draw the samples, as well.

hope this helps,

On 4/11/2013 9:38 AM, Jeph Herrin wrote:
My first thought is that you should calculate the predicted and expected from the same model, using -xtmelogit-; this is
done by calculating the fitted values with and without the random effects. This is, for example, how Medicare itself
does it when calculating predicted and expected rates for hospitals.

My second thought is that if you do have a reason to use -logit- to get the predicted values, then why not use the
predicted SEs to construct the CI? I usually do this by simulating p ~ N(xb,SE[xb]) for each observation, calculating
the inverse logit, and then using the order statistics (this is called parametric bootstrapping, I think).

But to answer your specific question - the only thing you are collecting from your bootstrap is -e(p)-, which is the
P-value for the chi2 test for the overall model. I think to do what you want (or what you think you want) you can't use
-bootstrap-, but will need to write your own bootstrap code - the crudest version being one which saves the predictions
from each sample and which you piece together later.

for b=1/100 {
  u data, clear
  logit transfer x1 x2 x3
  predict p_pred_`b'
  keep patientid p_pred_`b'
  save sample`b'

hope this helps,

On 4/11/2013 7:48 AM, Mohan, Deepika wrote:
Hello, I am trying to figure out how to generate confidence intervals around predicted probabilities at the patient
level, using bootstrapping. I am using Stata 12.0 on Windows.

I have a Medicare dataset which includes patient-level data, as well as hospital identifiers. The objective is to
assess hospital-level variation in the management of trauma patients.

I have calculated the expected probability of the outcome (transfer) for each patient:

. logit transfer x1 x2 x3 (where x are patient-level injury characteristics)
. predict p_exp,

As well as the predicted probability of the outcome (transfer) for each patient:

. xtmelogit transfer x1 x2 x3 || hospital_id:
. predict p_pred, mu

I am now trying to develop confidence intervals around those probabilities, and thought to use the bootstrap command.
For example,

bootstrap e(p), reps(10) saving(mydata): logit transfer x1 x2 x3

However, when I examine the saved data, what I see is a single predicted probability for each repetition and not 10
predicted probabilities for each individual. In other words, this command seems to be giving me confidence intervals
around the mean predicted probability rather than the predicted probability for the individual patient. Is there some
way to do this? I should also add that I don't have the ability to upload user programs like prvalue, since my
version of stata is run on a secure desktop (no web-access).

Any help would be greatly appreciated,

Thanks, Deepika Mohan MD MPH University of Pittsburgh Pittsburgh, PA 15261

* *   For searches and help try: * * *

*   For searches and help try:

*   For searches and help try:

© Copyright 1996–2016 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index