The CMS models for choosing comorbidities to include in a risk adjustment model for a quality measure are based on bootstrapping the regression 1000 times and choosing comorbidities (coded as clinical conditions with 0/1 coding of the presence of the condition for each patient, there are approximately 100 clinical conditions) for the final risk adjustment model if the p-value for the estimated coefficient on the comorbidity is statistically significant 90% of the time. Regardless of the wisdom of this strategy, I need to duplicate this methodology in data I have. In order to duplicate it, I need to set a seed, bootstrap the relevant regression, which will be either a logit for 0/1 outcome, or glm with log transform for cost outcomes, and recover the p-values for each covariate in the model for each iteration, so I can count if it is <0.05 in 90% of the cases. I am not sure how to bootstrap with replacement and save the p-values for each covariate from each iteration in a file or matrix so I can examine them. Any suggestions on how to code this would be appreciated.
Related Posts with Recovering p-values from multiple bootstrapped iterations of a logit or glm regression
Moderating effects in VAR possibleDear all, I am analyzing panel data covering 52 firms over one year and thinking about employing a …
Instrumental VariablesHello Experts, I am employing GMM approach on dynamic panel model. There are three endogenous variab…
Foreach in mataDear All, just a quick question. Is it possible to use foreach within a mata function? Suppose I h…
bootstraping clustered standard errors in system GMM help!Hi guys, I am running a diff-in-diff fixed effects model with 16 countries between 2002 to 2017 test…
Stata file to learn fromHello all, I am trying to learn Stata, and I was wandering if you have a Stata file (containing a sa…
Subscribe to:
Post Comments (Atom)
0 Response to Recovering p-values from multiple bootstrapped iterations of a logit or glm regression
Post a Comment