[R-sig-ME] Level 2 outcome and 'Downdated VtV' error
m@tthew@t@boden @end|ng |rom gm@||@com
Tue Jul 7 00:19:22 CEST 2020
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.
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