mixed effect models where time ordering is important

I'm confused by the second model, the LogDose = 0 if control seems
strange to me. How does it distinguish, in the intercept term, the
? control ? (0 + 0) and ? dose = 10^-1 ? (1 + -1) groups? And if the
intercept is monotonic with dose, wouldn't be an annoyance to have the
? dose = 0 ? just somewhere between other points, depending on the
exact doses used (and the log basis and the unit used...)?

? To model the dose response component and assuming a common time response
? shape on the log scale you might want to consider; 1). specify a dummy (1,0)
? for Control (0) vs Dosed (1) as "NotContr_d" (i.e. the intercept gives the
? control intercept), 2). LogDose  is set to zero for the control and is the
? log of the actual dose (low, medium, high) for the dosed treatments, and
? define Control_f <- as.factor(NotContr_d)  then 3). replace the above gamm
? with
? 
?  
? 
? gamm_01 <- gamm(formula = log(Weight) ~ NotContr_d + LogDose + s(time,
? by=Control_f, bs="cr"), data=data, random=list(subject=~1),  
? 
?      correlation=corCAR1(form= ~ time | subject))
? 
?  
? 
? The log transforms used above are just suggestions which can be compared to
? using raw Weights or Doses.
? 
?  
? 
?  
? 
? > 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
? 
?  
? 
? Dr Steven G. Candy
? 
? Director/Consultant
? 
? SCANDY STATISTICAL MODELLING PTY LTD
? 
? (ABN: 83 601 268 419)
? 
? 70 Burwood Drive
? 
? Blackmans Bay, TASMANIA, Australia 7052
? 
? Mobile: (61) 0439284983
? 
?  
? 
? 
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? 
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Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

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