Abstract
OBJECTIVE: A vast array of structural/imaging and clinical endpoints/outcomes are available today to osteoarthritis epidemiologists or trialists. Which assessments are best suited for which studies remains unsettled. When several assessments are available, these may be analyzed together (simultaneously or hierarchically), using statistical modeling and adjustment. Or, alternatively, they may be combined to form more complex multi-component or composite (potentially multi-domain) endpoints/outcomes. This review describes such concepts and their challenges, using examples from current osteoarthritis imaging research. DESIGN: A narrative, non-systematic literature search (PubMed and others) was conducted, and informal consultations were held with experts in the field. The identified concepts and experimental findings were then organized to present an integrated framework. RESULTS: Single imaging assessments can encompass one (uni-dimensional) or more (multi-dimensional) structures. Integration of image assessments of one structure/tissue across anatomical locations provides aggregate measures. This can also be created across heterogeneous (multi-dimensional) types of assessments (multi-component/composite), either within an area (such as imaging - single domain) or across broader areas of health and well-being (multi-domain). Weighting, standardization, and (clinical) usefulness are crucial characteristics of multi-component/composite endpoints. Examples of these concepts are here provided in the context of osteoarthritis imaging. CONCLUSIONS: Options for multi-component/composite endpoints in osteoarthritis research are virtually infinite. Smart research strategies are required to explore and validate these vast possibilities, with appropriate statistical treatment being paramount. A one-size/endpoint-fits-all approach will likely fail in observational and interventional studies. Imaging assessment needs to be tailored to both the drug's unique mechanism of action, and to the participants' morpho-type.