[R] analysing mixed effects/poisson/correlated data

[Alexandra Bremner]

I am attempting to model data with the following variables:

 

timepoint   - n=48, monthly over 4 years 

hospital - n=3

opsn1 - no of outcomes

total.patients

skillmixpc - skill mix percentage

nurse.hours.per.day

 

Aims

To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients).

To determine if nurse.hours.per.day affects rate.

To determine if rates vary between hospitals.

There is first order autoregression in the data. I have attempted to use the lmer function (and lmer2) with correlation structure and weights:

 

test1 <-lmer(opsn1~timepoint+as.factor(hospital)+ skillmixpc + nursehrsperpatday +(timepoint|hospital) +offset(log(totalpats)),family=poisson, data=opsn.totals)

 

test2 <-lmer(opsn1~timepoint+as.factor(hospital)+ skillmixpc + nursehrsperpatday +(timepoint|hospital)+offset(log(totalpats)),family=poisson, data=opsn.totals, correlation=corAR1(form=~1|hospital))

 

test3 <-lmer(opsn1~timepoint+as.factor(hospital)+ skillmixpc + nursehrsperpatday +(timepoint|hospital)+offset(log(totalpats)),family=poisson, data=opsn.totals, correlation=corAR1(form=~1|hospital),weights=varIdent(form=~1|hospital))

 

Test1 & test2 give the same output (below). Does this mean that the correlation structure is not being used?

Test3 produces the following error message (I notice there are others who have had problems with weights).

Error in model.frame(formula, rownames, variables, varnames, extras, extranames,  : 

        variable lengths differ (found for '(weights)')

 

> summary(test1)

Generalized linear mixed model fit using Laplace 

Formula: opsn1 ~ timepoint + as.factor(hospital) + skillmixpc + nursehrsperpatday +      (timepoint | hospital) + offset(log(totalpats)) 

   Data: opsn.totals 

 Family: poisson(log link)

   AIC   BIC logLik deviance

 196.2 223.0 -89.12    178.2

Random effects:

 Groups   Name        Variance   Std.Dev.   Corr  

 hospital (Intercept) 3.9993e-03 6.3240e-02       

          timepoint   5.0000e-10 2.2361e-05 0.000 

number of obs: 144, groups: hospital, 3

 

Estimated scale (compare to  1 )  1.057574 

 

Fixed effects:

                      Estimate Std. Error z value Pr(>|z|)  

(Intercept)          -2.784857   1.437283 -1.9376   0.0527 .

timepoint            -0.002806   0.002709 -1.0358   0.3003  

as.factor(hospital)2 -0.030277   0.120896 -0.2504   0.8022  

as.factor(hospital)3 -0.349763   0.171950 -2.0341   0.0419 *

skillmixpc           -0.026856   0.015671 -1.7137   0.0866 .

nursehrsperpatday    -0.025376   0.043556 -0.5826   0.5602  

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

Correlation of Fixed Effects:

            (Intr) timpnt as.()2 as.()3 skllmx

timepoint   -0.606                            

as.fctr(h)2 -0.382  0.132                     

as.fctr(h)3 -0.734  0.453  0.526              

skillmixpc  -0.996  0.596  0.335  0.715       

nrshrsprptd  0.001 -0.297  0.204 -0.130 -0.056

 

Can anyone suggest another way that I can do this analysis please? Or a work around for this method? 

Any help will be gratefully received as I have to model 48 different opsn outcomes.

 

Alex