Creating a Nomogram with Multiply Imputed Datasets

Hello,

My team is hoping to create a nomogram with multiply imputed data using the nomogram function from the rms package.

We can create a full nomogram with a single dataset, and we can create a partial nomogram from the imputed dataset by pooling results using fit.mult.impute from the Hmisc package. However, we can’t figure out how to create a pooled survival curve for the multiply imputed data that we can use for our nomogram.

Looking around online, I wasn’t able to find any solid answers, but I found some emails with Dr. Harrell from 2013 that seemed to indicate that there wasn’t a set method for how to combine results across survival curves for each imputed dataset. Given that that post is more than 10 years old at this point though, I wondered if there has been any movement on that front that I haven’t been able to find.

So, I wanted to ask if there is a way to create a nomogram from multiply imputed data in the rms package, or, if not, if there is a way to compute a survival curve from multiply imputed data either with or without rms and use that survival curve to complete the nomogram created using fit.mult.impute.

Thank you for your time,
Gregor

It’s still a good question. While you can average survival curve estimates, stacking of all the imputed datasets and doing one large model fit, ignoring the falsely low standard errors and p-values, will probably work best. I do some other types of analysis with this approach in Regression Modeling Strategies - 24  Bacteremia: Case Study in Nonlinear Data Reduction with Imputation .