[R-sig-ME] Level 2 outcome and 'Downdated VtV' error
Patrick (Malone Quantitative)
m@|one @end|ng |rom m@|onequ@nt|t@t|ve@com
Tue Jul 7 15:16:04 CEST 2020
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).
> >-----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
> >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.
> R-sig-mixed-models using r-project.org mailing list
Patrick S. Malone, Ph.D., Malone Quantitative
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