[R-sig-ME] Using lme4 to predict probability of appendicitis

[David Duffy]

On Mon, 31 Jan 2011, Dale.W.Steele at gmail.com wrote:

> I'm interested in modeling the probability of appendicitis in patients
> with abdominal pain.
>
> The R binary data file 'http://www.ped-em.org/appy.rda' contains
> the following variables from a pilot study of 138 children with
> abdominal pain.
>
> 'dx' eventual diagnosis: 0=no appendicitis, 1=appendicitis
> 'gender' Male/Female
> 'wbc' total white blood cell count
> 'priorprob' Clinical predicted probability of appendicitis
> 'doc' doctor who assigned 'priorprob'
>
> My initial thought was to fit a multiple logistic regression model:
>
> m1 <- glm(dx ~ gender + priorprob + wbc + doc, family=binomial, data=appy)
>
> However, it seems likely that each doctor interpreted the probability scale
> differently. The 23 doctors evaluated from 1 to 17 patients each. I'm
> not primarily interest in predictions by a specific clinician. Thus,
> it seems to make sense to fit a generalized linear mixed model.

I think this is a fun kind of dataset.  I don't think there is a large 
amount of "slop" coming from the different clinicians -- I looked at this 
by a cheap and nasty mixed model of priorprob as a continuous variable, 
noting dx was not correlated with doc.  It's amusing that there is 
little correlation between wbc and priorprob.

> m2 <- glmer(dx ~ priorprob + gender + wbc + (1 | doc),
> family=binomial, data=appy)
>
> m3 <- glmer(dx ~ priorprob + gender + wbc + (priorprob | doc),
> family=binomial, data=appy)

These seemed OK to me.  You might look at a GAM too.

Just 2c, David Duffy.

-- 
| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v