In clinical trials, we often compare two treatment groups using repeated binary measures over time. In such trials, we may encounter missing observations, adverse side effects, or non-responsiveness to therapy which for ethical reasons, may result in increased medical intervention beyond the protocol therapy. We developed a family of statistical tests based on the Wilcoxon statistic which orders the vectors of repeated binary observations and events where the ordering is determined by 'clinical relevance'. For some scenarios, clinically meaningful ordering of the vectors may be defined by a natural algorithm, while for other scenarios the ordering is obtained from a group of clinicians. We present the statistical development of the proposed method, effects of the variability of rankings among clinicians, examples of the application of the proposed method using data from a clinical trial on otitis media, and simulation studies comparing the statistical power of the proposed method to more traditional methods of analysis. Our simulation studies indicate that the proposed method is competitive with and, for some scenarios, is preferable to the traditional methods. Although the proposed method is not applicable to every situation, we believe that for some diseases and scenarios, this simple method is noteworthy in the sense that it can be adjusted to extremely complex situations if vectors can be hierarchically ordered in a reasonable fashion, it can be focused on alternatives that have high clinical relevance, and it can be readily adapted to accommodate non-protocol 'outcomes' and missing data. The public health relevance of this study is that clinically meaningful results can be targeted in clinical trials
