[R-sig-ME] Making sure something is kosher

[Mitchell Maltenfort]

This is looking at patient outcomes with doctor as random effect

Some patient demographics are missing

I figured this wasn't going to be as easy as pool glmer imputations from
mice, then use se.ranef from arm to get standard errors on random effects

I was hoping to get away with 1000 bootstrapped samples, 5-10 imputations
each bootstrap

Too optimistic?

On Wednesday, August 12, 2015, Paul Johnson <pauljohn32 at gmail.com> wrote:

> I think you mean to ask, how does one pool bootstrapped confidence
> intervals constructed from 20 imputations? Same question about variance
> estimates, or ICC. Appears now, each of those is a separate battle.
> Similarly, any anova() test done separately. Rubin's rules great for MLEs.
> Not so much for all that other stuff that we do.
>
> VanBuren worked out the R^2 and some papers exist on model chisquare.
> if you find one on CI, let me know. I'm wondering if you could simply
> aggregate runs and build a CI. if not, appears you need better math stat
> training than I have.
>
> One project here explored that. If you are in the "I need 100 imputations"
> crew and you want 5000 bootstraps, well... get a cluster. I'll ask that
> team what they know.
>
> pj
> Paul Johnson
> http://pj.freefaculty.org
> On Aug 11, 2015 7:06 PM, "Mitchell Maltenfort" <mmalten at gmail.com
> <javascript:_e(%7B%7D,'cvml','mmalten at gmail.com');>> wrote:
>
>> I realize combining multiple imputation with random effect estimation is
>> dodgy.
>>
>> Supposing I use mice with glmer to do the model fits, and then bootstrap
>> to
>> get confidence intervals on the random effect estimates.
>>
>> Is that a clean way to do it?  If not, what's recommended?
>>
>> Thanks!
>>
>>
>>
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