Are assumptions linear mixed model necessary?
I have four groups:
- Patients with R, who receive treatment A
- Patients with R, who receive treatment B
- Patients without R who receive treatment A
- Patients without R who receive treatment B
I would like to investigate if R status, treatment, and time influence the health utility score (EQ5D). The EQ5D is measured at 4 timepoints: time at inclusion (baseline), 30 days, 90 days, and 180 days.
I am working with RStudio. However, my statistical knowledge is not sufficient enough. As I understand correctly, I am supposed to do a lineair mixed model, where I test the three groups together:
fit_1 <- lme(
EQ5D ~ R * Treatment * FollowupDays + covariates,
data = data,
na.action = na.omit,
random = list(
Institute = ~ 1 + FollowupDays,
Participant.Id = ~ 1 + FollowupDays
)
)
To check my assumptions, I used
plot(fit_1)
qqline(resid(fit_1))
Levene.Model <- lm(fit_3b.Res2 ~ Treatment, data = data)
However, non of these assumptions are met. The residual plot do not look great and the Levene's test suggests heteroscedasticity (with a very low p-value). But I have read that mixed models do not require homoscedasticity in the same way as a simple linear regression, and that variance can be modeled directy by using:
weigths = varIdent()
My question: Are these assumptions checks necessary for mixed models or is it acceptable to proceed with this model even if the classical linear regression assumptions aren't met? If not, should I use a different model for EQ5D or can I alter my model in a way that my assumptions are met? Thank you in advance !
Below you find the plots:
Posted Jan 27, 2026, 11:32 AM UTC