[R-sig-ME] Random intercepts and/or slopes

[Gabriel Baud-Bovy]

On 10/10/2013 12:39 PM, Maria Helena Mourino Silva Nunes wrote:
> Dear all,
>
> I’m new to mixed models, and I’m analysing a patients’ data set whose response variable is adherence/no adherence to medication (binary variable, 0/1). For each patient there are different types of independent variables: sociodemographic; clinical; blood pressure control; etc. Additionally, patients are grouped (nested) within doctors. Due to the sampling design, I only have one replicate for each patient.
>
> I want to study the impact of the independent variables on the adherence/no adherence to medication.  I’m not interested in studying the patients by themselves.
> My question is: should I consider the simplest model     response  ~  Indep. Variables +  (1|Doctor),
> or  should I explicitly introduce the nested structure of patient within doctor,   response ~ Indep. Variables + (Patient|Doctor) ?

Hi Helena,

Here a some syntax for your random effects assuming that all IV are
between-subject factors

  response  ~  Indep. Variables +   (1|Patient)

response ~ Indep. Variables + (1|Doctor) + (1|Patient)

The last one allow to define random effects for differences
across doctors as well as patients (with lmer, patients need to be uniquely
identified)

If you have repeated-measure (within-subject factors), then
things become more complex as the effect of some factor might
also be different across patients.  With lme, one advise in this
case is to include a maximal random structures (Barr et al., 2012, 2013)

(all within subject factor |Patient)

it one is interested only in fixed effects. However, other expert
recommend introducing random effects only for factors with a sufficient
large number of levels because variance estimates might not be
reliable. In any case, the situation with binomial model might be
different.

As you will see, there is not (yet) a generally agreed methodology
with mixed-effect models, the way you have it with old-fashioned ANOVA.
It takes a deep understanding of the methods and your data to
decide exactly what to do. There are now quite a bit of books and
papers on GLMM that might help to learn more on this topic.

Best,

Gabriel


>
>
> Thanks in advanced!
>
> Best regards,
> Helena.
>
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>


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