# Multiple Imputations and Survival Analysis

I’m new to using multiple imputations and I would like an opinion on using it with survival analysis in R. I am using MICE on an entire dataset. For one of my independent variables I decided to place an NA for the observations that were outliers influencing my results as determined by diagnostics. I decided to impute because there are a large number of missing observations on a different independent variable. Is it appropriate to impute data that I coded as NA because they were influential outliers?

I am estimating a Weibull AFT survival model. How do you derive model fits (e.g. pseudo R-squared, log-likelihood, AIC) with pooled imputation data? Finally, how do you pull the scale parameter?

BTW, this is the code I am using to pull my pooled results

summary(pool(fitm))

• No. It is not acceptable to corrupt your data to get rid of "outliers". The outliers were telling you the model didn't fit. "Diagnostics" do not tell you that the data was wrong. Instead they tell you where the model does not fit. – DWin Jun 16 '15 at 0:59
• You can delete outliers and including them can lead to erroneous conclusions. First, outliers do not necessarily bias against your theoretical expectations. Second, there is a school of thought which suggests they are not representative of the larger population and they can violate an assumption in your model. If you are transparent in your report, then it is acceptable to remove them. – Dyllan Jun 16 '15 at 2:12
• They taught you that in business school? – DWin Jun 16 '15 at 2:26

Second, when you say "How do you derive model fits (e.g. pseudo R-squared, log-likelihood, AIC) with pooled imputation data?" it seems that you are trying to do a single model fit on a pool of your multiple imputations. What you instead do is fit your model individually to each of your multiple imputed data sets. The results of the multiple models are then pooled; the differences in results among the different models/imputations help indicate the variation due to the imputation process. The rms package in R has facilities for handling survival analysis of multiply imputed data sets.