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Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering patient preferences at the individual level. In this article, we propose a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. Stratified randomization can be performed based on the participants’ baseline outcome rankings. A Wilcoxon-Mann-Whitney U-statistic is used to average the pairwise DOOR between one treated and one control patient, considering the difference in these patients’ ranking of outcome importance. We use both theoretical and empirical methods to examine the statistical properties of our method and to compare with conventional approaches. We conclude that the proposed composite DOOR properly reflects patient-level preferences and can be used in pivotal trials or comparative effectiveness trials for a patient-centered evaluation of overall treatment benefits.

Publisher Summary Marked differences exist among groups that can be sorted according to descriptors such as race or ethnicity. Large degrees of individual variation exist even within racial and ethnic groups. This chapter attempts to address the observed trends in bone dynamics that can be sorted by race or ethnic grouping. Sorting of humans, however, suffers from the inconstancy of racial and ethnic definitions. This chapter, therefore, defines the terms used to describe human groups and then explores their application to the epidemiology of hip fracture and parameters that affect skeletal health. The diagnostic term “osteoporosis” refers to a skeletal condition that predisposes to multisite fragility fractures. Because only hip fractures reliably lead to hospitalization and therefore documentation, discussion presents hip fracture data in various populations as a reflection of morbidity resulting from osteoporosis. Discussion concludes that bone mass, in and of itself, is not the best predictor of fracture risk in all groups. Variables such as bone geometry, reproductive history, physical activity, dietary exposures, body composition, and others all contribute to fracture risk, reflecting the rich diversity peculiar to the human race.