# Linear mixed model for 3 time points

**URL:** <https://discourse.datamethods.org/t/linear-mixed-model-for-3-time-points/5753>\
**Category:** modeling strategy\
**Tags:** longitudinal\
**Created:** [June 15, 2022, 11:46am UTC](https://discourse.datamethods.org/t/linear-mixed-model-for-3-time-points/5753 "2022-06-15T11:46:33Z")\
**Posts on this page:** 1\
**Showing post:** 9

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**Author:** ![MSchwartz](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/mschwartz/32/2579_2.png) [@MSchwartz](https://discourse.datamethods.org/u/MSchwartz)\
**Post date:** [June 16, 2022, 6:32pm UTC](https://discourse.datamethods.org/t/linear-mixed-model-for-3-time-points/5753/9 "2022-06-16T18:32:27Z")

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@JorgeTeixeira see inline below:

> [@](#):
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> 1. Do you think it is consensual that y0 should be a predictor? I keep listening to conflicting recommendations here.

I am not sure that there is complete consensus on anything in statistics.

That being said, the use of the baseline measurement as a covariate, that is, as an “adjustment” for any baseline imbalance between the groups, even in a randomized study, is, in my mind, the preferred approach and there have been numerous publications on this topic. In essence, in a repeated measures setting, it is an extension of the traditional ANCOVA approach, where the usual OLS model formulation would be:

FU ~ Group \* Baseline

Where FU is the single follow up measurement (one record per patient), Group is the treatment group factor and Baseline is the baseline measurement.

There is a recent thread regarding the use of change from baseline in this forum that raises related issues and has numerous references to the use of ANCOVA:

[Changing the “Status Quo” analyses for Regulatory Submission - Change from Baseline](https://discourse.datamethods.org/t/changing-the-status-quo-analyses-for-regulatory-submission-change-from-baseline/5725)

There is one reference listed therein:

[https://www.tandfonline.com/doi/abs/10.1080/10543406.2011.550113](https://www.tandfonline.com/doi/abs/10.1080/10543406.2011.550113)

that you may find of particular interest in the context of a repeated measures setting, such as you have.

> [@](#):
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> 1. I have never seen before a model as you have mentioned. Can you explain the rationale behind of using " Group:T1"

As per the ANCOVA formulation above, it is the explicitly stated interaction term between the treatment group and the baseline measurement. Rather than using, in R syntax, the more concise:

Group \* Baseline

which embodies both the main effects and the interaction terms, it is using the longer syntax of:

Group + Basline + Group:Baseline

to more explicitly express the model terms.

> [@](#):
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> 1. Of those I have listed, I presume you would opt for M3 or M4?

My own preference would be M4, for the reasons we have been discussing, where, as I noted in my reply, you have two, not three, records per patient, given the timing of your observations.

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