PRM79 Integrating Patient Preferences and Clinical Trial Data in a Bayesian Model for Quantitative Risk-Benefit Assessment

indicates the categories of a chosen cycle or could refer to additional attributes of the patients like age or sex. RESULTS: State probability and survival curves merely show specific aggregates of the data while classic Markov trace visualizations with for example bubble diagrams do not visualize data in a sense that would facilitate a detection of proportions and trends. Applying Parallel Sets to analyze Markov models provides an interactive visualization technique where changing the reference Markov cycle is as easy as highlighting particular dimensions, thus enabling the exploration of the progress of patient cohorts with certain characteristics through the model. CONCLUSIONS: Model development always requires thorough analysis of its structure, behavior and results. Parallel Sets enable an intuitive and efficient visualization technique for presentation purposes as well as exploratory analysis.