The best way of handling the situation of missing data is not to encounter it, by making sure that all data is collected.
Some options for handling missing data include:
(1) If the patient is available and the parameter is stable for the patient (for example, height), then measure the parameter now.
(2) If the probable value is within a limited range, then run the model with values within that range. The predicted values may be close enough that a precise value is not needed.
(3) Select another model that runs with the data that you do have.
Janssen et al reported 6 statistical methods for handling missing data. Most of the methods required access to the derivation data and/or the ability to recalculate the regression coefficients. The "multiple imputation" method (multiple imputations are used to imputer a missing value) provided a result closest to the true value.
The more data that is missing, the less likely that the estimate will be appropriate.
