Purpose: The purpose of this study is to illustrate the impact of ignoring missing data in follow-up studies and to provide a hierarchical Bayesian approach to simultaneously estimate rates and missing data probabilities.
Methods: To account for missing data in follow up studies, a hierarchical Bayesian procedure is proposed and investigated via simulation.
Results: A simulation study demonstrates the impact of ignoring missing data on inferences in terms of bias and in ranking populations in terms of risk. An example of rates of disabilities for various German construction worker professions also illustrates the usefulness of the method.
Conclusions: Use of a hierarchical Bayesian approach allows for flexible modeling of rates and data availability.