Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 21 Revised: June 2015 9.7. Immune Monitoring I mmune Monitoring data will be presented in the data listings. 10.Quality of Life S ubject- reported quality -of-life (QOL) data will be presented in the data listings. 11. Radiology R adiological assessments will be performed at Screening, Week 13, Week 25 EOT, and at the 6-month long term follow-up visit. R adiology data will be presented in the data listings. 12.Interim Analysis T his section describes the interim analysis plan (IAP) to be executed by the independent statistician and assessed by the DMC. The specific IAP results are closed to the sponsor unless one of the criteria are met, and even then the DMC will use its discretion regarding the communications of results to the spo nsor, with patient safety balanced against benefit being the primary concern. This IAP consists of the ability to detect early evidence of efficacy and also futility. The statistical computations used to devise this IAP take into account the possibility of a delayed effect of the same magnitude as used to plan the trial size but the statistical tests will ignore the possibility of delayed effect because there cannot be certainty that a delayed effect exists. Stated another way, the statistical tests to be done as specified in the IAP are optimal in cases where the hazard ratio is proportional; the alternative is intractable because it would be to use parametric statistics in a situation where the underlying form of the failure -time distribution is not known. Thus, if the possibility of a delayed effect were not taken into account and it actually exists then the IAP would have lower power to detect early efficacy than anticipated because early events would not be discriminatory for efficacy, and a conclusion of futility would have increased power because early events would be give a false signal about lack of efficacy. By contrast, if the possibility of delayed effect were taken into account when a delayed effect does not exist then the IAP will have greater po wer than anticipated to detect early efficacy whereas futility would have diminished power than anticipated. T he IAP is designed so that interim analyses for a comparison are to be executed at 40%, 60%, and 80% information times, corresponding to 214, 321, and 427 deaths, with the final analysis at 534 deaths as previously discussed. T he one- sided significance criteria to be used for early detection of efficacy analyses are 0.000135, 0.00147, 0.00501, respectively, with the adjusted final analysis to use 0.01063. These significance levels were computed using SAS PROC SEQDESIGN as one- sided O’Brien - Fleming boundaries with an overall one-sided type I error probability of 0.0125. Following is the SAS code used to compute these intervals. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 22 Revised: June 2015 proc seqdesign boun daryscale=pvalue; design nstages=4 info=cum(0.4 0.6 0.8 1) method=obf alt=upper stop=reject alpha=0.0125 ; run; T he early detection of efficacy is to use the same statistical testing methodology as specified for the primary analysis. The statistical characteristics for the interim analysis event- driven time - points and significance levels are computed using simulations. The simulation methodology for delayed effect is described in A ppendix A. The following table shows the statistical operating characteristics of this plan based on 10,000 simulation replicates. For example, when the alternative hypothesis is true (average hazard ratio = 0.68) then at the second interim analysis is projecte d for 3.2 years following the start of randomization and the estimated cumulative probability of rejecting the null hypothesis is 0.1571 (with confidence interval as shown). Also shown is the largest hazard ratio found for which the null hypothesis was rej ected (rej HR), showing that at this interim analysis a hazard ratio less than or equal to 0.716 will pass the criterion for early efficacy (an estimate of the critical hazard ratio). Please note that these simulations are consistent with the targeted over all one-sided type I error of 0.0125: for analysis 4 (“#” column) the proportion of trials rejected (“rejected” column) under the null hypothesis (row with “null” in the “hyp” column) is 0.0127, and is close enough to 0.0125 considering computations were done using simulation. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 23 Revised: June 2015 Statistical Operating Characteristics of the Interim Analysis Plan Simulation estimates (cumulative, # replications = 10000): # events hyp time test rejected 95% CI rej HR 1 214 null 2.5 futile 0.0771 0.0719 to 0.0825 >=1.212 superior 0.0000 0.0000 to 0.0004 <=0.000 alt 2.5 futile 0.0054 0.0041 to 0.0070 >=1.216 superior 0.0065 0.0050 to 0.0083 <=0.603 2 321 null 3.0 futile 0.2297 0.2215 to 0.2381 >=1.090 superior 0.0013 0.0007 to 0.0022 <=0.712 alt 3.2 futile 0.0071 0.0055 to 0.0089 >=1.093 superior 0.1571 0.1500 to 0.1644 <=0.716 3 427 null 3.7 futile 0.4224 0.4127 to 0.4322 >=1.023 superior 0.0045 0.0033 to 0.0060 <=0.779 alt 4.0 futile 0.0078 0.0062 to 0.0097 >=1.032 superior 0.5417 0.5319 to 0.5515 <=0.779 4 534 null 4.6 superior 0.0127 0.0106 to 0.0151 <=0.819 alt 5.0 superior 0.8388 0.8314 to 0.8460 <=0.819 Notes: •Delayed effect: HR = 1 for 4 months, four month linear ramp to HR = 0.68, with HR = 0.68 goi ng fo rward. •Overall α = 0.025 (0.0125 for each comparison) •~85% target power •The total accrual period is 2.5 years with a 19 month linear ramp to constant accrual (800 in eac h c omparison, 400 per arm) •Control arm median = 22 months T he significance level to be used for detection of futility at each interim analysis is 0.00001. This futility significance level was chosen in order to provide a balance between the sought for estimated probability of detecting futility and protecting against falsely declaring futility. A preceding table also shows the statistica l operating characteristics of futility detection. For example, when the null hypothesis is true (hazard ratio = 1) then at the second interim analysis is projected for 3.0 years following the start of randomization and the estimated cumulative probability of rejecting the futility null hypothesis is 0.2297 (with confidence interval as shown). Also shown is the smallest hazard ratio found for which the futility null hypothesis was rejected (rej HR), showing that at this interim analysis a hazard ratio great er than or equal to 1.090 will pass the criterion for futility (an estimate of the futility critical hazard ratio). The futility null hypothesis is H 0: ∆ ≤ 0.68 and the futility alternative hypothesis is H A: ∆ > 0.68, where ∆ is the hazard ratio. The testi ng of the futility null hypothesis using SAS is computed as follows. The futility null hypothesis can be rewritten using the natural logarithm as H0: ln(∆ ) ≤ ϕ, or H 0: βarm ≤ ϕ), or H 0: βarm - ϕ ≤ 0. w here ϕ = ln(0.68) and βarm is the logarithm of the hazard ratio associated with the experimental arm. The futility null hypothesis is tested using SAS PHREG using the OFFSET option. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 24 Revised: June 2015 T he possibility of there being no delay of effect can be assessed through simulation where the delay of effect is eliminated. The following table shows the statistical operating characteristics of the IAP when no delay of effect is applicable. When there is no delay of effect there is considerably more power to detect early efficacy (a similar outcome was observed for the trial planning computations). Statistical Operating Characteristics of the Interim Analysis Plan for No Delay of Effect Simulation estimates (cumulative, # replications = 10000): # events hyp time test rejected 95% C I rej HR 1 214 null 2.5 futile 0.0780 0.0728 to 0.0834 >=1.213 superior 0.0002 0.0000 to 0.0007 <=0.600 alt 2.6 futile 0.0000 0.0000 to 0.0004 NA superior 0.1902 0.1825 to 0.1980 <=0.605 2 321 null 3.0 futile 0.2249 0.2167 to 0.2332 >=1.090 superior 0.0008 0.0003 to 0.0016 <=0.716 alt 3.3 futile 0.0000 0.0000 to 0.0004 NA superior 0.6745 0.6652 to 0.6837 <=0.717 3 427 null 3.7 futile 0.4232 0.4135 to 0.4330 >=1.024 superior 0.0051 0.0038 to 0.0067 <=0.778 alt 4.1 futile 0.0002 0.0000 to 0.0007 >=1.043 superior 0.9218 0.9164 to 0.9270 <=0.779 4 534 null 4.6 superior 0.0119 0.0099 to 0.0142 <=0.819 alt 5.1 superior 0.9854 0.9829 to 0.9877 <=0.819 Notes: •No delayed of effect: HR = 0.68 for all time •Overall α = 0.025 (0.0125 for each comparison •~85% target power •The total accrual period is 2.5 years with a 19 month linear ramp to constant accrual (800 in eac h c omparison, 400 per arm) •Control arm median = 22 months T here are IAP issues that require special consideration because of the nature of this study: The events needed for each of the two comparisons may not occur at the same time. The IAP for an interim analysis and the final analysis is to be implemented only when the required numbers of events have been reached for both comparisons. M ore than the required number of events may be applicable at the time of an interim or final analysis. The definitive analysis will be based on backing up the data until the time point at which the required number of deaths is first applicable. An analysis using all data available will also be executed as a supplemental analysis. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 25 Revised: June 2015 The DMC will be required to use its discretion regarding the action to be taken in case one comparison meets criterion at an interim analysis whereas the other comparison does not. The issue is whether an action for the comparison meeting criterion would adversely affect the ability to obtain a definitive answer to the comparison not meeting criterion.