[R-sig-ME] Using individual differences from model A as predictor in model B

Thierry Onkelinx thierry.onkelinx at inbo.be
Fri Dec 2 13:23:23 CET 2016

Dear Koen,

I think you can fit this in a single model. Here a a few options:

with lme4:
Adherence ~ BeforeAfter * Treatment + (1 + BeforeAfter|Participant)
Adherence ~ BeforeAfter * Treatment + (1| Participant:BeforeAfter)

with INLA:
Adherence ~ BeforeAfter * Treatment + f(Time, model = "rw1", replicate =

The BeforeAfter:Treatment interaction is the effect you are interested in.
The lme4 random effect allow for an additional treatment effect for
individual participants. The INLA random effect allows for correlated
random intercept along Time for the individual participant. rw1 stands for
random walk of order 1, which models the differences between consecutive
time points.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-12-02 12:03 GMT+01:00 Koen Neijenhuijs <kn.journal.news op gmail.com>:

> Dear all,
> we've run an experiment with two groups, which we followed for 3 weeks.
> Each participant got three trials per week, and our dependent variable is
> the adherence, defined as whether they replied to the trial or not. In the
> third week, we introduced a manipulation, which was balanced across the two
> groups. We want to test the effect of the manipulation, moderated for
> intrinsic motivation to adhere to the trials. We are struggling with the
> operationalization of intrinsic motivation.
> We ran a binomial mixed-effect model on the data of the first two weeks, to
> estimate intrinsic motivation. So far, we've come up with three methods to
> do so, but each comes with their own concerns. I was hoping to hear your
> thoughts on this.
> 1. The first method is simply to use the aggregated (sum) adherence of each
> participant. This method would be seemingly valid, as the model on the
> first two weeks shows no main effect of time, group, nor the interaction
> time*group. However, I am reluctant to go this route as this method is less
> detailed than the other options.
> 2. The second method is to extract the random-adjusted intercept and
> random-adjusted slope of time (random effects + fixed effects), per
> participant. The interaction of these two represent intrinsic motivation as
> it inherits both the intercept of adherence as well as its' development
> over time; this combination is capable of representing every possible
> motivation timeline (start high and go lower over time; start high and stay
> high over time; start low and go up over time; etc). However, using this
> method, to test the effect we're interested in will result in a three-way
> interaction (intercept*slope*manipulation), and a four-way interaction to
> check moderation of prior group characteristics. It is unlikely we have
> enough power to test this, as our sample size is limited.
> 3. The third method is to extract the prediction equation from the model of
> the first two weeks and apply this to the data of the third week. This
> method will give us one representation of motivation instead of two, which
> does include both fixed and random effects. However, as the method is
> applied to data of the third week, I am uncertain whether it is valid as a
> representation of intrinsic motivation over the first two weeks.
> Sorry for the long wall of text. What are your thoughts on this? Are there
> other ways of operationalizing individual differences on adherence in the
> first two weeks to use as an independent variable on adherence in the third
> week?
> Cheers,
> Koen
>         [[alternative HTML version deleted]]
> _______________________________________________
> R-sig-mixed-models op r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list