2006. •When there are competing events then censoring competing events provides a between - arm test of the event of interest: Prentice et al. 1978. T he hazard ratio for each comparison will be estimated using stratified (STRATA <stratification - variable>;) proportional hazard regression with the specification of ties handled by the exact method (TIES=EXACT option on MODEL statement). Two-sided 95% confidence interval will be reported in order to maintain consistency with the reporting of estimates from other prostate cancer studies. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 14 Revised: June 2015 8.2.3. Survival Graphs T he survival data will be plotted as either Kaplan- Meier or cumulative incidence distribution estimates depending on the amount of competing events. The cumulative incidence estimates and not the Kaplan- Meier estimates will be plotted if the percent of subjects with competing events in either arm exceeds 3%. When there are competing events then the Kaplan -Meier estimate is not an estimate of the probability free of event: Gooley et al, 1999. 8.2.4. Other Analyses of Survival T he following other analyses, supporting, sensitivity, or exploratory, will be performed: •Deaths that occur after the cutoff will not b e censored. •Clinical site homogeneity will be assessed by including in the model site grouping multi- level main effect terms and also interaction terms for arm by categorical site grouping. •The analysis will be re -run with arm by stratum interaction added to the model statement in order to assess arm by stratum interaction. If the 2 -sided P value for this multiple degree-of- freedom test of homogeneity is significant at two -sided 0.10, then the stratum- specific hazard ratio estimates and confidence intervals will be computed. If the statistical criterion is not met then the test of homogeneity will be regarded as an exploratoryanalysis of the data collected. Thus, the outcome of the strata homogeneity test will notaffect the interpretation of significance f or the primary test, but will affect the way the results are presented or subsequently analyzed. •Effect modifier analyses will be performed for putatively prognostic factors, includingthose explored previously in the literature as well as those hypothesized to be predictivein the context of this study. Effect modification will be assessed one factor at a time instratified models. Each factor will be analyzed as either a natural dichotomy, adichotomized ordered categorical with cutpoint chosen based on biologic considerations,or a continuous variable dichotomized at the overall median. A separate effect sizeestimate for arm and its 95% confidence interval will be computed for each level of thedichotomy using contrast statements in SAS. •The four Kaplan- Meier estimates (arm by factor) for each of the factors analyzed as effect modifiers will be plotted on one graph. •Regression models with multiple added prognostic covariates will be estimated. The covariates to be added will include those explored previously in the literature as well as those hypothesized to be predictive in the context of this study. Interactions will also beexplored for contribution to the model. •Subjects randomized and not initiating study intervention will be added to the FAS and the primary analyses repeated. •An analysis of the relationship between dose intensity and outcome by arm will be performed. The main method will be a time- dependent covariate that increases by one for each injection. The interaction between arm and this time- dependent covariate will be the focus of this analysis. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 15 Revised: June 2015 8.3. Secondary Endpoint (Alive Without Event [ AWE] at Six Months ) T he testing of AWE will have meaning in the context of type I error probability control only if the corresponding primary survival compa rison meets its formal statistical criterion. The formal significance levels used for survival analysis will be used for AWE. A WE has less statistical sensitivity than an analysis based on the closely related and traditional time-to-progression because the timing of events is ignored. Thus, AWE is inherently conservative. This trade- off is acceptable because the trial size provides adequate sensitivity for meaningful differences in the AWE proportions for each comparison. The approximate statistical sensitivity of AWE can be illustrated by assuming a simple comparison of proportions between the arms being compared. Assume a significance level of one -sided 0.0125 and that the control arm proportion is 0.5. Each comparison is based on 800 subjects, 400 per arm. The probability is 90% or greater that this statistical test will be significant if the true proportion difference is 0.126 or greater and the critical difference is 0.080. T he 95% confidence interval on the odds ratio estimate will be computed as the measure of the magnitude of effect. A ll analyses of AWE will be conducted on the ITT set and repeated for the FAS as a sensitivity analysis. 8.3.1. AWE Analysis Method T he AWE endpoint will be analyzed using stratified logistic regression. The estimated odds ratio will be greater than unity if the experimental arm has a more favorable outcome. U sing the estimated odds ratio and a reference specification for the proportion of subjects with AWE in the control arm the expected proportion of subjects with AWE f or the investigational arm can be projected, and thus the projected between- arm difference in proportions computed. If the statistical criterion is met then a test for homogeneity of the odds ratio across strata will be performed by adding arm by stratum interaction to the above model. If the P value for this multiple degree -of-freedom test of homogeneity is significant at two -sided 0.10, then the stratum -specific odds ratios and confidence intervals will be computed using the ESTIMATE feature of PROC LOGI STIC. If the statistical criterion is not met then the test of homogeneity will be regarded as an exploratory analysis of the data collected. Thus, the outcome of the homogeneity test will not affect the interpretation of significance for the primary test, but will affect the way the results are presented or subsequently analyzed. 8.3.2. Other Analyses of AWE T he following other analyses, supporting, sensitivity, or exploratory, will be performed: •Clinical site homogeneity will be assessed by including in the model site grouping multi- level main effect terms and also interaction terms for arm by categorical site grouping. •The analysis will be re -run with arm by stratum interaction added to the model statement in order to assess arm by stratum interaction. If th e 2-sided P value for this multiple Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 16 Revised: June 2015 degree-of- freedom test of homogeneity is significant at two -sided 0.10, then the stratum- specific hazard ratio estimates and confidence intervals will be computed. If the statistical criterion is not met then the test of homogeneity will be regarded as an exploratory analysis of the data collected. Thus, the outcome of the strata homogeneity test will not affect the interpretation of significance for the primary test, but will affect the way the results are presented or s ubsequently analyzed. •Effect modifier analyses will be performed for putatively prognostic factors, including those explored previously in the literature as well as those hypothesized to be predictive in the context of this study. Effect modification will be assessed one factor at a time instratified models. Each factor will be analyzed as either a natural dichotomy, adichotomized ordered categorical with cutpoint chosen based on biologic considerations,or a continuous variable dichotomized at the overal l median. A separate effect size estimate for arm and its 95% confidence interval will be computed for each level of thedichotomy using contrast statements in SAS. •Regression models with multiple added prognostic covariates will be estimated. The covariates to be added will include those explored previously in the literature as well as those hypothesized to be predictive in the context of this study. Interactions will also beexplored for contribution to the model. •Subjects who had not been previously observed to have an event and who miss their sixmonth disease assessments are recoded as AWE = 1 (instead of AWE = 0) for both arms,and then for each arm separately. •Subjects who had not been observed previously to have an event and who have assessment(s) that are not in the window for the six month assessment are coded according to the closest assessment, for both arms, and then for each arm separately. •The analysis of progression will be subjected to a tipping point analysis as described in Yan et al. 2009.