6% and a risk factor prevalence of 50%. While having a sample size toward this lower estimate would still be adequate to detect these moderate to large effects, it would require us to be parsimonious in testing variables to avoid over-fitting (only about 60 stroke outcomes in PFO patients). After final database construction, we will consider whether to model the risk of the composite outcome stroke and transient ischemic attack (TIA). This has an outcome rate of 11.2% on the sample of our database with completed follow-up, virtually doubling the number of outcomes. Aim 4. To develop a composite index that can rank patients with CS and PFO by their conditional probability that the PFO was pathogenically-related to the index stroke (Model #1) and the risk of stroke recurrence (Model #2) For Aim 4, we will combine our probability models in order to stratify patients by their risk of stroke recurrence conditioned on the probability that the PFO is pathogenically-related (i.e. not incidental) to the index stroke. Thus, the aim of this step is to combine models to estimate the PFO-attributable recurrence risk. This step prepares us for Aim 5: stratification of the clinical trial populations by this attributable recurrence risk, which we hypothesize corresponds to stratification by expected-benefit. In performing Aim 5, it is important to recognize that subgroup analyses are prone to false positive results due to multiple comparisons. Indeed, an advantage of multivariable risk-based analysis is that it allows the influence of multiple variables to be summarized