Sorry for asking my question even though I know there are some subjects about mixed effect model on the forum. But I think my question is somewhat different.
I have to answer to a question about repeated measure.
It is a group of people followed for a treatment against depression: 146 people (Men an women), 8 times of measure for each subject. I have to answer about if treatment works better in one gender group compare to the other.
My variables of interest are
ScoreHamilton (Score used to assess depression state),
GROUPE (Gender: male or female),
TEMPS (Different times of visit),
NUMERO (Subjects ID)
I know I have to used mixed effect model, but I am not sure if my scripts (below) are correct.
modMix_H0 <- lme(ScoreHamilton ~ TEMPS + GROUPE, random = ~1+TEMPS|NUMERO, data = Ham_norm.mix)`
I fitted variables
TEMPS (time) and
GROUPE (Gender) like fixed effects and
NUMERO (Subjects) like random effect. I am wondering if that is right.
I hesitate a little about the way I made random effect. I tried to do random intercept and random slope like this
~1+TEMPS|NUMERO cause I noticed that people making random effects used to do like this
~1+TIME|ID (in general). Now I am wondering why I cannot put in random terms my variable
GROUPE, something like this
~1+GROUPE|NUMERO, or like this
The other part of my question is the interpreting of the output. Here are the results of the summary of the model:
Linear mixed-effects model fit by REML Data: Ham_norm.mix AIC BIC logLik 6628.782 6663.471 -3307.391 Random effects: Formula: ~1 + TEMPS | NUMERO Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 4.73695760 (Intr) TEMPS 0.08200003 -0.353 Residual 4.72718973 Fixed effects: ScoreHamilton ~ TEMPS + GROUPE Value Std.Error DF t-value p-value (Intercept) 22.989933 0.5959364 905 38.57783 0e+00 TEMPS -0.352266 0.0109268 905 -32.23866 0e+00 GROUPEHomme 2.952001 0.8013428 144 3.68382 3e-04 Correlation: (Intr) TEMPS TEMPS -0.359 GROUPEHomme -0.652 0.012 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.66404151 -0.58774912 0.02206275 0.56281247 3.97325207 Number of Observations: 1052 Number of Groups: 146
I don't know how to interpret all of the parameters, how they could influence the interpreting of my final result (that is, the impact of
GROUPE on the Score Hamilton), and the quality of my model .
Though, the way I interpret this result is that the score is significantly higher in men (
Homme) than in women. So, the treatment improve better the mental state in women (lowest score), a result I was not expecting for. This make me wondering about about the way I computed the model.
I have additional questions. My variable
VISIT was factor which I turned into numeric. Could it change something whether my variable
VISIT is factor or numeric?
Could it change something about my results whether I used
na.omit or not in the model, since my dataset has a lot of missing values?