I have a question about the use of lme for hypothesis testing. I am
interested in the effect of treatment on my response variables
(potassium) in this case - I have about 50 blood variables total. My
basic design is:

>
> $ ID: Factor w/ 36 levels  --> RANDOM EFFECT
> $ EXPOSURE : int  -> FIXED FACTOR (REPEATED MEASURE)
> $ TREATMENT: Factor w/ 2 levels "Control","Experimental":  --> FIXED FACTOR
> $ SEX : Factor w/ 2 levels "Female","Male": --> FIXED FACTOR
> $ K  : num  2.2 2.3 2 3.5 ... --> RESPONSE VARIABLE
>
> I am unsure how to proceed to test for the effect of treatment on my
> response as I've had several opinions leading to different results. It
> was first suggested to me that my loaded model (in terms of fixed
> factors) should be the final step, and I should look at the p-values
> from the summary table which in this case yields a significant effect
> of exposure:treatment, as well as the treatment main effect.
>
> M2K.lme<-lme(K~ SEX*EXPOSURE*TREATMENT, random=~1|ID, method='REML',
> weights=varIdent(form=~1|EXPOSURE),data=biochem12)
> > summary(M2K.lme)
>
>                                         Value        Std.Error    DF
>  t-value      p-value
> (Intercept)                            3.101693   0.260219   112
> 11.91955   0
> SEX                                    -0.63323    0.344237    25
>  -1.83951    0.0777
> EXPOSURE                          -0.04875    0.026876   112   -1.81398
>  0.0724
> TREATMENT                       -0.7661      0.354619    25    -2.16034
>  0.0405
> SEX:EXPOSURE                    0.057346  0.035553   112    1.612949
> 0.1096
> SEX:TREATMENT                  0.758975   0.476787   25     1.591854
> 0.124
> EXPOSURE:TREATMENT         0.091248  0.036626   112    2.491379   0.0142
> SEX:EXPOSURE:TREATMENT  -0.08805    0.049243   112   -1.78812     0.0765
>
> Although this makes sense, I am concerned that by over-fitting the model
> like this (the 3-way interaction especially) I am not getting an
> accurate result.
>
> I was told my another to continue with backwards selection of my fixed
> effects which, in the case of potassium means I remove every
> interaction and main effect leaving an empty model! While that may be
> true biologically speaking (if none of my factors impact potassium
> whatsoever) it makes for an awkward interpretation of my results.
>
> I also have the opposite problem with several variables whereby with
> backwards selection non significant terms becomes significant - which
> doesn't necessarily make the evidence much stronger in the biological
> sense.
>
> I would be incredibly grateful for a further opinion on how to proceed!
>
> Elizabeth
>

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