I have a medical dataset with approx 200 variables. One of the variables is a bio-marker (concentration of a particular enzyme). It's distribution is right skew, and the problem is that values above a certain level are censored/cut off at that level. So while the mean of the variable is around 10, any values greater than 50 are recorded as 50.
I would like to impute continuous values for those censored values. I am using multiple imputation with the mice package in R at present, though other systems are available to me and I am open to other approaches. A thought I had was to recode all those censored values to be missing and then running the imputations. If any of the imputed values that were originally censored are below the cut-off, then they will then be assigned to be the cut-off value.
I'd like to know opinions about this, and/or any better methods of dealing with this.