[R-sig-ME] c++ exception with logistic glmer

[Cole, Tim]

David,

I agree that the model can be fitted as an interval-censored time-to-event (actually it's a mix of left, right and interval-censored), but that does not make my approach wrong. In fact it is better in two respects.

First, bone maturity score is subject to measurement error, so it's possible (though rare) to transition from 0 to 1 and then back to 0. Survival analysis treats such points as missing, when they are a valid representation of measurement error.

Second, I am interested in the between-subject SD of the time to maturity, obtained by dividing the random intercept SD by the age coefficient. To my knowledge this is not available from the survival analysis.

Whether or not you are persuaded by these arguments, the fact remains that my two questions about glmer remain valid - its behavour is odd.

Best wishes,
Tim
---
Tim.Cole at ucl.ac.uk<mailto:Tim.Cole at ich.ucl.ac.uk> Phone +44(0)20 7905 2666 Fax +44(0)20 7905 2381
Population Policy and Practice Programme
UCL Institute of Child Health, London WC1N 1EH, UK

From: David Duffy <David.Duffy at qimr.edu.au<mailto:David.Duffy at qimr.edu.au>>
Date: Thursday, 8 January 2015 05:32
To: Tim Cole <tim.cole at ucl.ac.uk<mailto:tim.cole at ucl.ac.uk>>
Cc: "r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>" <r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>>
Subject: Re: [R-sig-ME] c++ exception with logistic glmer

On Tue, 6 Jan 2015, Cole, Tim wrote:

I'm returning to a thread I started a year ago. My logistic glmer model
[...]B
and Jonathan French and Ben suggested an alternative time-to-event
analysis instead of the logistic.

As now described, your problem is clearly interval censored time-to-event,
and your logistic model is just not the right approach - bone maturity is
an irreversible state (consider what happens to your age regression
coefficient if you add in more ages before or after maturity).  Either do
the survival analysis, which gives you the median age at maturity, or fit
a (nonlinear) growth model to maturity score.

| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au<mailto: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


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