While the success of clinical trials hinges on the results of the entire study population, there is also significant interest in understanding how endpoints vary within subgroups defined by key demographic factors or disease characteristics. This can help identify patients who benefit more from new treatments or those at greater risk for safety issues. Furthermore, because the size of individual subgroups in clinical trials is a factor beyond the investigator's control, identifying the existence and size of potential subgroups is also a crucial task.
This presentation introduces Subgroup Screening, a new feature in JMP Clinical. We also discuss several considerations for subgroup analysis using JMP—such as the limitations of the "By" role, the newly added "Subgroup" role within the Response Screening platform, and the utility of UpSet plots for summarizing subgroups. Finally, we illustrate this methodology with a case study using data from patients with mild to moderate Alzheimer's disease.
