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This is my first question after sign in:

when I read a paper, the author said:

"To explore the influences of X(2 group:X-,X+) on the longitudinal change in the above "OUTCOMES", we fitted linear mixed effects models to characterize individual paths of change.These models had random intercepts and slopes for time and an unstructured covariance matrix for the random effects and included the interaction between (continuous) time and X as predictor. Age, gender, education, were covariates. "

the dataset "df" is something like:

ID time Age gender edu X OUTCOME
1 0 90 Female 18 X- 200
1 1 90 Female 18 X- 205
1 2 90 Female 18 X- 205
1 3 90 Female 18 X- 210
2 0 80 male 10 X- 100
2 1 80 male 10 X- 115
2 2 80 male 10 X- 135

... ...

every row is one visit of one participant, "id" is the participant id, "time" is the visit year from the baseline "0" , "X" is categorical "X- or X+ "

My syntax of "lme":

model1 <- lme(OUTCOME ~ time * X + Age + edu + gender, random=~time|ID, data=df)

if the above one is correct, the result of Fixed effects: "time:X+" is my result for time related association of OUTCOME between group X- and X+ ? and Age edu gender are adjusted for the result of X ?

Thank you!

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1 Answer 1

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For the model:

model1 <- lme(OUTCOME ~ time * X + Age + edu + gender, random=~time|ID, data=df)

is the above one is correct

It is consistent with the description in the question, yes. However, please note that it is common for the time variable to be centred in this type of modelling. From the description, there is no way to know if that did that.

time:X+" is my result for time related association of OUTCOME between group X- and X+ ?

Yes, that is correct.

and Age edu gender are adjusted for the result of X ?

Yes !

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  • $\begingroup$ Does this answer your question ? If so please consider marking it as the accepted answer, and if you haven't already please consider upvoting it. If not, please let us know why ? $\endgroup$ Commented Jun 26, 2021 at 12:18

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