A clinician I am working with has indicated interest in developing a clincial prediction model. Their data contain missing information for some of the covariates as well as the outcome. I know I can use multiple imputation via `rms::aregImpute`

to address the issue of missing data.

One of my main concerns is the calibration of the model. This paper by Van Calster et.al reccomends using penalized regression in order to prevent overfitting. I know `rms`

has an implementation for penalization via the `pentrace`

function, but `pentrace`

does not seem to accept a model fit via `fit.mult.impute`

, returning the following error:

Error in pentrace(f2) : fitter not valid

What are my options here to introduce some moderate penalization into the model while accommodating imputation? Is this possible via `rms`

or is this a scenario where I would “roll my own” estimator?

Your input is appreciated and I’m willing to share relevant details where needed.