Clinical trials in psychiatry inherit methods for design and statistical analysis from evidence-based medicine. However, trials in other clinical disciplines benefit from a more specific relationship between instruments that measure disease state (e.g. biomarkers, clinical signs), the underlying pathology and diagnosis such that primary outcomes can be readily defined. Trials in psychiatry use diagnosis (i.e. a categorical label for a syndrome) as a proxy for the underlying disorder, and outcomes are defined, for example, as a percentage change in a univariate total score on some clinical instrument. We label this approach to defining outcomes weak aggregation of disease state. Univariate measures are necessary, because statistical methodology is both tractable and well-developed for scalar outcomes, but we show that weak aggregate approaches do not capture disease state sufficiently, potentially leading to loss of information about response to intervention. We demonstrate how multivariate disease state can be captured using geometric concepts of spaces defined over routine clinical instruments, and show how clinically meaningful disease states (e.g. representing different profiles of symptoms, recovery or remission) can be defined as prototypes (geometric locations) in these spaces. Then, we show how to derive univariate (scalar) measures, which capture patient’s relationships to these prototypes and argue these represent strong aggregates of disease state that may be a better basis for outcome measures. We demonstrate our proposal using a large publically available dataset. We conclude by discussing the impact of strong aggregates for analyses in traditional and novel trial designs.