I have a large water chemistry dataset where my response variable (radium-226 concentration) has two types of missing data. Sometimes it is left censored, sometimes the value is reported, but most often (>99% of the time) the response is missing apparently at random (MAR). I have a reasonable estimate of the value below which the response variable is left censored. Also there is a high rate of predictor variables missing at random.
I am wondering how to proceed. There are R packages that do Tobit censored regression, like censReg and NADA. There are also R packages like mice and Hmisc / rms that can do MI on MAR data. If I ignore the data where the response variable is MAR, then I loose 99% of the observations, but I could then treat the remaining observations in a survival model. A package like Hmisc & rms can do imputation on my MAR predictor variables.
Are there R packages that can deal with both censoring and MAR?
Resppected authors such as Frank Harrell suggest dropping variables that have more than about 20% MAR prior to MI, so I wonder is it just silly to try to do MI on a response variable when more than 99% of the time it is MAR?
Any thoughts you have would be greatly appreciated. I am a statistics student and newbie at dealing with missing data.