[R-sig-ME] mixed effect models where time ordering is important

Gosse, Michelle Michelle.Gosse at foodstandards.gov.au
Tue Apr 21 22:14:22 CEST 2015


Hi all,

I have repeated measures weight data on rats who were in a 28-day toxin study. I have one control group and the toxin was administered at one of three doses (low, medium, high). This is not a cross-over design, so (for example) the rats who were in the low dose group always got the low dose over the course of the study.

The rats were not fully grown when the study started. Body weights were measured every fourth day.

The interest is in seeing if the toxin has an influence on body weight. I am looking at using lmer to analyse this data, however I am unsure how to handle the ordering of time, as this will be correlated with increasing body weight.

If I did not have to worry about time ordering, I thought this model would work:

Weight ~ dose + (1|subject)

The doses are being treated as fixed effects as I am not wanting to extrapolate the impact of dose beyond what was administered in the study.

I was wondering if the appropriate model for my data would be:

Weight ~ dose * time + (1|subject)

However, time is measured as days from initial dose administration (e.g. day 1 = first day of dosing). While the rats are all very similar in age, I do not believe they were all born on the same day, and so I am unsure about time as a proxy for age (assuming an intercept in the model). And day is measured discontinuously (every fourth day). I feel that omitting day will remove one obvious explanatory variable from the model, which may bias the results as well as producing a model that poorly fits the data.

I have tried to find an example of a toxicology study that uses a mixed effects model in R on repeated measures, that specifies the model. I have been unable to locate one.

I would appreciate any advice/recommendations on how to handle this data. I have already advised that a series of separate ANOVAs are not statistically defensible given that the weights are likely to be auto-correlated and the statistical analysis needs to account for this.

Cheers
Michelle, note: I do not work Fridays


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