replacement algorithms, or whether a component database needs to be excluded--although statistical methods to preserve data and power will be fully employed (such as imputation for independent variables). Because missingness of the dependent (outcome) variable poses more potential problems due to informative censoring, we intend to exclude any datasets for which less than 90% of patients have follow-up at one year. Re-adjudication of all stroke outcomes is planned as part of the RoPE study. Methods ensuring database quality during database construction will be complemented by steps taken during the predictive modeling process to check for consistency of effects across databases. Aim 2: To identify patient characteristics associated with the presence (versus the absence) of a PFO, and to use this "PFO propensity" to estimate the patient-specific probability that a PFO was pathogenically related to the index stroke. (Model #1) Since PFOs discovered in patients with CS may be pathogenically related to the stroke or not, in Aim 2, we will use logistic regression models to identify patient characteristics that are associated with the presence of a PFO among patients with CS ("PFO propensity"). In previous work [ 7 , 11 , 21 ], we have shown that this "PFO propensity" can be related to the probability that a PFO is pathogenic (i.e. non-incidental) using Bayes' theorem and 2 simple assumptions: 1) if not for those strokes that are PFO-attributable, the probability of a PFO in a CS patient would be the same as in the general population (controls); and 2)