LAUSR

Missing data is much studied in epidemiology and statistics. Theoretical development and application of methods for handling missing data have mostly been conducted in the context of prospective research data, and with a goal of description or causal explanation. However, it is now common to build predictive models using routinely collected data, where missing patterns may convey important information, and one might take a pragmatic approach to optimising prediction. Therefore, different methods to handle missing data may be preferred. Furthermore, an underappreciated issue in prediction modelling is that the missing data method used in model development may not match the method used when a model is deployed. This may lead to over-optimistic assessments of model performance.