The publicly available data I used
I addressed the unbalanced followup visits by transforming the visit time to the nearest multiple of 8 then de-duplicated visit times.
I then used last observation carried forward to impute missing log CD4 values. Then ran two linear mixed intercept models (log CD4 ~ time) on the raw data and on the imputed data. I checked the residual plots and I got these.
So I see that my imputation method has made some of the residuals more normal, but why is there that huge group of fitted values around 3? How would I be able to address that?
The models I fit:
raw_3_model <- lme(log_CD4_1 ~ Time, random = ~Time|ID, data = raw_data, method = "REML", na.action = na.exclude) treatment_3_model <- lme(measurements ~ time, random = ~time|ID, data = final_3, method = "REML", na.action = na.exclude)