Hi!

I have longitudinal data of abdominal surgery patients at 4 timepoints (baseline, 1-month, 3-month, 6-month). The response is the EQ visual analogue scale (EQ VAS), which ranges from 0 to 100. The 3 groups for comparison are mild-to-moderate anemia, mild anemia and non-anemia. I have used the generalized least squares and followed the guide from the rms book/short course.

Here is the code that I have used.

```
a <- Gls(vas ~ anemia * month +
vas0 + rcs(age, 4) + sex + type + lap_open, data=df,
correlation=corCAR1(form = ~month | ic))
```

where, `vas`

is the vas score at follow-up (i.e. 1-month, 3-month and 6-month), `anemia`

is the 3-level severity of anemia (i.e. mild-to-moderate anemia, mild anemia and non-anemia), `month`

is integer value (i.e. 1, 3, 6) `vas0`

is the baseline vas score, `age`

is age of patient at baseline, `sex`

(female, male), `type`

is either gastro, hepa or uro/gynae, `lap_open`

(i.e. either surgery is laparoscopic or open) and `ic`

is the unique patient identifiers. I assume linearity for `month`

. In the book, it stated “Time is modeled as a restricted cubic spline with 3 knots, because there are only 3 unique interior values of week.” Here, what does it mean by interior values?

When I plot the variogram, it gives me only 2 points. How do I assess this?

When I plot the variance and normality assumptions, the residuals do not look random and the qqplot does not seem to follow the theoretical normal distribution. Does this suggest that I abandon the GLS? What statistical method should I use here?

Besides EQ VAS, I would also like to look at the individual domains of the EQ-5D-3L (ordinal variable with 3 levels) and utility score (range from 0.854 for state 11121 to -0.769 for state 33333). Would it be useful to look at Semiparametric Ordinal Longitudinal Models?

Thank you!