In an era where medicine is increasingly personalized, clinical trials often suffer from small samples. As a consequence, treatment comparison based on the data of these trials may result in inconclusive decisions. Efficient decision-making strategies are highly needed so decisions can be made with smaller samples without increasing the risk of errors. The current chapter centers around one such strategy: Including information from multiple outcomes in the decision, thereby focusing on data from binary outcomes. Key elements of the approach are (1) criteria for treatment comparison that are suitable for two outcomes, and (2) a multivariate Bayesian technique to analyze multiple binary outcomes simultaneously. The conceptual discussion of these elements is complemented with software to implement the approach. To further facilitate trials with small samples, the chapter also outlines how interim analyses may result in more efficient decisions compared to the traditional sample size estimation before data collection.