Assessing "health" and its determinants often requires integrating several ordinal and binary outcomes. In the absence of a "gold standard", traditional methods to justify a linearizing transformation and relative weight for each outcome fail, although "more" can be assumed to be "better". Recently, a non-parametric approach has been generalized to multivariate data. U-scores for multivariate data (mu-scores) are the first "intrinsically valid" approach, because they utilize only information that is invariant to (monotonous) scale transformations and (positive) weights. It is demonstrated how the "muStat" package (cran.r-project.org) and server (muStat.rockefeller.edu) can be applied to objectively integrate censored (time-to-event), longitudinal, and hierarchically structured (e.g., as risk, health, quality-of-life, cost) data, thereby providing a sound basis for policy decisions.