[R-sig-ME] measuring carry-over effects in mixed models

Phillip Alday me @end|ng |rom ph||||p@|d@y@com
Wed Mar 2 06:12:50 CET 2022

Depending on the exact nature of the carryover effect, the usual
suspects would be:

1. using an autoregressive model of some type
2. including lead/lag predictors in your model, e.g.
	lmer(y ~ time*group*prev_time + [covariates] + (1 | student))

But note that the initial timepoint doesn't have a prev_time and so
there is a missing value there.


On 17/2/22 12:32 pm, Simon Harmel wrote:
> Hello All,
> I'm analyzing the data from my longitudinal study whose design can be
> depicted as (view the following in plain text):
> O1  X1  O2  X2  O3  X3  O4
> O1      O2      O3      O4
> where Xs denote subtitled videos given to the treatment group and Os
> denote measurement occasions.
> My current model is:  lmer(y ~ time*group + [covariates] + (1 | student))
> However, I'm also interested in measuring the "carry-over effects" of
> watching subtitled videos at each occasion to the subsequent
> occasions.
> For instance, I want to know how much watching the subtitled video at
> the first occasion (O1) impacts the treated students' performance at
> later occasions (O2 and O3) etc.
> I wonder if any changes to my model can enable me to measure these
> carry-over effects or if any other R package may provide such
> functionality?
> Many thanks,
> Simon
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