Measures to reduce the impact of missing data on the reference standard data when designing diagnostic test accuracy studies

Despite efforts to determine the presence or absence of the condition of interest in all participants in a diagnostic accuracy study, missing reference standard results (i.e. missing outcomes) are often inevitable and should be anticipated in any prospective diagnostic accuracy study. Analyses that include only the participants in whom the reference standard was performed are likely to produce biased estimates of the accuracy of the index tests. Several analytical solutions for dealing with missing outcomes are available; however, these solutions require knowledge about the pattern of missing data, and they are no substitute for complete data. In this presentation we aim to provide an overview of the different patterns of missing data on the reference standard (i.e. incidental missing data, data missing by research design, data missing due to clinical practice, data missing due to infeasibility), the recommended corresponding solutions (i.e. analytical correction methods or including a second reference standard), and the specific measures that can be taken before and during a prospective diagnostic study to enhance the validity and interpretation of these solutions. In the presentation various examples will be discussed. Researchers should anticipate the mechanisms that generate missing reference standard results before the start of a study, so that measures and actions can explicitly be taken to reduce the potential for biased estimates of the accuracy of the tests, markers, or models under study, as well as to facilitate correction in the analysis phase. In all cases, researchers should include in their study report how missing data on the index test and reference standard were handled, as invited by the STARD reporting guideline.