# Best way to handle na values in regression predictor variable

I am running a multinomial logistic regression model in R (using the multinom function from the nnet package) with a set of 12 predictor variables. Some of these variables measure the time duration from a reference point until the occurrence of a particular event in experimental trials. In instances where the specified event did not occur, an na has been entered into the table. This affects a significant number of observations.

My understanding is that na values should be appropriately treated prior to running a regression model. But in this case it is not clear to me how best to handle the "missing" data. Setting them to zero implies that the event happened instantaneously, when in reality it has not happened at all. Replacing na values with mean/median is also unsatisfactory, as the model should capture non-occurrences of specific events. Removing these observations from the model excludes too many data points.

What would be the "best" way to proceed in this case?

• Have you checked other questions tagged as missing-data? There is no single best approach, this depends on your particular problem. – Tim Feb 21 '18 at 17:10
• I've not been able to find something addressing the problem as I have presented it here. I appreciate that there is no single solution but input from the community would be useful in deciding the best course to take. I am currently looking at whether converting the variable to a categorical one could work. – allhands Feb 21 '18 at 18:42