dealing with zero inflation in the regression model I have a longitudinal data, with different follow-up number for individuals. I have considered measurements for each individual as a curve which I already smoothed them, then calculated the area under curve and divided by the interval length to find the average of the curve over a year. My aim is to see if the area is different among individuals with and without cancer. I am doing a mixed model regression using lmer() from lme4 package in R.
Note: individuals with Area = 0 are those that have only one follow-up, so that is why there is no curve and area is zero. But I do not want to remove them as they can contribute to the margin.
Problems:

*

*My outcome distribution is as shown in the picture below is not normal.

*My outcome is not count otherwise I could do zero-inflated Poisson model.

My model is using lmer is as below:
Libraray(lme4)
lmer.model = lmer(Area ~ cancer+age+gender+education+(1 | sibling id), data = dat, REML = FALSE)

I got a suggestion to try:
library(gamlss)
library(gamlss.inf)
gamlss.model <- gamlssZadj(y=Area, mu.formula=~ cancer+age+gender+education, family=GA,trace=TRUE)
summary(gamlss.model)

The problem is that the gamlssZadj() dose not account for dependency in my moded which comes from the siblings. Is there any suggestion.


glmmTMB allows for mixed effect models with zero-inflation. Check: https://cran.r-project.org/web/packages/glmmTMB/index.html
