Analytic Standards 8.1.1. Data Cutoff Date A ssociated with each analysis is a data cutoff date. The key analyses will use the data cutoff date to censor event dates, and this may result in an event date turning into a censored date. Additional sensitivity analyses without data cutoff censoring will also be done when there has been data cutoff censoring, especially when data cutoff censoring results in loss of events. 8.1.2. One-sided P Values S tatistical programs most often report 2 -sided P values, but the criterion for the key analyses described herein are based on 1- sided P values associated with 1 -sided null hypotheses. The computation of a 1- sided P value requires that the effect size estimate be available and that the favorable direction be known, that is, the direction indicated in the associated al ternative hypothesis. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 12 Revised: June 2015 Assume that P 2 is the 2 -sided P value reported from a statistical program. The one- sided P value, P 1, is to be computed as follows: If the effect estimate is consistent with favorable outcome then P 1 = P 2/2, otherwise P 1 = 1 – P 2/2. 8.1.3. Assessing Clinical Site Homogeneity I t is important to assess clinical site homogeneity with respect to the primary and secondary outcomes. Following is the method that will be used to obtain a descriptive assessment of clinical site homogeneity. A ssume each clinical site is identified by a unique code. It is often the case that one or more clinical sites will fail to have at least one subject assigned to each arm. All such clinical sites are to be pooled into a new pseudo clinical site with a new unique code. This new pseudo clinical site is to be used in the following analyses. A forest graph with sites or a grouping of sites will be created where the effect estimate will be displayed along with the 95% confidence interval. This graph is most effectively displayed if the estimates are sorted by estimate size.