I have a run a linear mixed effects model in R to model clinical data, however this model is heteroscedastic (as there excess zeros in the response variable)....

I have tried transforming the data (log transform) and (sqrt), however neither transformation resolve the issue (see residual versus fitted value plot). I have not used cox proportional hazards model as the data is not time-to-event data, the data measures force and there are a large number of observations have a reading of zero. I cannot exclude these readings as they are valid.

I have found a R package that runs Tobit regression (AER), however this will not accommodate the random effects in the model. I cannot find any R packages that run Weibull mixed effects models (or gamma mixed effects models)...

Does anyone know if there is a package to run these type of models? (or can they suggest any alternative approach).

Many thanks

Etn

I have a run a linear mixed effects model in R to model clinical data. However, this model is heteroscedastic (as there excess zeros in the response variable).

I have tried transforming the data (log transform) and (sqrt). Still, neither transformation resolves the issue (see residual versus fitted value plot). I have not used Cox proportional hazards model as the data is not time-to-event data, the data measures force and there are a large number of observations have a reading of zero. I cannot exclude these readings as they are valid.

I have found an R package that runs Tobit regression (AER). Nevertheless, this will not accommodate the random effects in the model. I cannot find any R packages that run Weibull mixed effects models (or gamma mixed effects models)...

Does anyone know if there is a package to run these type of models? (or can they suggest any alternative approach).

Many thanks

Etn