nested mixed effects logistic regression binomial glm)results differ by function.

Thank you so very much to everyone responding to my request.  I learnt a
lot.  You helped me figure out my mistake.  I wanted to adjust for the
correlation inside households.  Most of the households, however, contained a
single individual.  When I combined them into a single cluster, the answers
were exactly what I needed.
Regards
Lize


-----Original Message-----
From: David Duffy [mailto:David.Duffy at qimr.edu.au] 
Sent: 24 April 2015 05:12
To: Lize van der Merwe
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] nested mixed effects logistic regression binomial
glm)results differ by function.

I have a dichotomous outcome on 2500 individuals. From 18 geographical 
areas, and many households nested within areas. I need to assess the 
association between various predictors and my outcome, adjusting for 
the correlation within households, as well as within areas. The 
following R functions provide dramatically different results.

glmer(CC~predictor+1|area/household,family=binomial)
glmmPQL(CC~predictor, random=~1|area/household),family=binomial)

PQL is known to be biased, the amount depending on a few things including
the proportion CC in the sample, and number of levels for the REs. You could
try hglm (package hglm, using EQL) and see how different the results are
from that ;)  It is also possible one or both programs encountered numerical
problems because of features of your data. If you can send your original
data, or simulated data of the same structure (that gives a similar
problem!), we could have a look.

Cheers, David.

| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: 
| -0101 Genetic Epidemiology, QIMR Berghofer Institute of Medical 
| Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A