Abstract
BackgroundThe diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study.MethodsTheoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples.ResultsPositive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies.ConclusionsInclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.
Highlights
The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing
For each value of the disease prevalence, the expected positive-predictive value (PPV) of the DB study increases with increasing values of DLR+
For each DLR+ value, the expected PPV of the DB study increases with increasing disease prevalence
Summary
The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies This is timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. [† Search query for Title/Abstract: ("database study" OR "database studies") AND ("claims" OR "administrative"); Search date: 06 JAN 2021] In such times of change, it is important to make renewed efforts to promote good practice in the planning, conduction, analysis, and interpretation of DB studies. Outcome validation studies are important for DB studies based on secondary use DBs, such as administrative claim DBs. This paper focuses on how to utilize the existing validation studies to inform and evaluate the design of a new claim-based DB study in its planning phase. The steps after the conduct of the DB study, which may include bias adjustments using the data from the validation studies, are out of the scope of this paper
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