[R-sig-ME] Level 2 outcome and 'Downdated VtV' error

[Matthew Boden]

Thank you for these responses. I figured this was the case (that you
shouldn't predict a Level 2 variable in a mixed model), but followed
contrary advice from a colleague.  Appreciate the help.

Matt

On Tue, Jul 7, 2020 at 6:16 AM Patrick (Malone Quantitative) <
malone using malonequantitative.com> wrote:

> Agreed with the others. Chiming in only because I've recently been
> doing research on such aggregation and I can say the consensus seems
> to be it doesn't introduce bias (with the possible exception of very
> small clusters, which you don't have).
>
> On Tue, Jul 7, 2020 at 6:40 AM Viechtbauer, Wolfgang (SP)
> <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >
> > Hi Matt,
> >
> > What you are trying to do (i.e., use a level 2 variable as the outcome)
> can and should not be done. The outcome in a multilevel model needs to be
> measured at the lowest level.
> >
> > In your model (A1), we know a priori that there is 0 within-station
> variability. Hence, the ICC is exactly equal to 1 in that model, but trying
> to fit such a model pushes the optimization routines into a situation that
> leads to degeneracies.
> >
> > The only way to get around this is to aggregate the data to the level of
> the outcome (i.e., use PopCov as the outcome and aggregate all other level
> 1 predictors to level 2 means).
> >
> > Best,
> > Wolfgang
> >
> > >-----Original Message-----
> > >From: R-sig-mixed-models [mailto:
> r-sig-mixed-models-bounces using r-project.org]
> > >On Behalf Of Matthew Boden
> > >Sent: Tuesday, 07 July, 2020 0:19
> > >To: r-sig-mixed-models using r-project.org
> > >Subject: [R-sig-ME] Level 2 outcome and 'Downdated VtV' error
> > >
> > >Good afternoon,
> > >
> > >I am looking for advice regarding a multi-level model I am trying to
> > >implement using lme4. My two-level random-effects model won’t run,
> perhaps
> > >due to one or two issues.
> > >
> > >Background: Level 1 is patients, which are clustered in healthcare
> > >facilities (‘Station’). The outcome is a continuous variable (‘PopCov’)
> > >that is calculated at the facility-level, and is thus a Level 2 variable
> > >that does not vary at the patient level.
> > >
> > >The aim of this analysis is to examine whether PopCov is predicted by
> (a)
> > >patient-level (e.g., race/ethnicity, age, symptom severity), and (b)
> > >facility-level variables (e.g., overall racial/ethnic composition,
> average
> > >age). It is important to examine factors such as race/ethnicity at both
> > >patient and facility-levels because patients with different
> racial/ethnic
> > >backgrounds tend to differ in terms of age, symptom severity, etc.
> > >
> > >Each record/row in my data is a patient, with facility-level variables
> > >(including PopCov) having identical values among patients within a given
> > >facility.
> > >
> > >An error is thrown when I run a basic model.
> > >
> > >A1 <-lmer(PopCov ~ (1 | Station), data = DISP)
> > >
> > >*Error in fn9nM$xeval()) : Downdated VtV is not positive definite
> > >
> > >I obtain the same error when I add to the model either a patient-level
> or
> > >facility level predictor.
> > >
> > >An internet search suggested that I have complete separation of my data
> > >and/or poorly scaled variables.
> > >
> > >I assume this issue has to do with the fact that the outcome is a level
> 2
> > >variable. Perhaps compounding the issue is the large and unbalanced
> nature
> > >of the data. I have ~6 million patients clustered in ~1000 healthcare
> > >facilities. Individual facilities have anywhere from 100 to 30000
> patients
> > >clustered in them.
> > >
> > >I could use some advice regarding how to specify the model to predict a
> > >facility-level variable (level 2) from both patient (level 1) and
> > >facility-level (level 2) variables with these data.
> > >
> > >Thank you in advance.
> > >
> > >Matt
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
>
> --
> Patrick S. Malone, Ph.D., Malone Quantitative
> NEW Service Models: http://malonequantitative.com
>
> He/Him/His
>

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