Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry.

Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.