This software is quite general and has been under development since the 1980s. Validation has included comparison of results from other software used for trial planning, including SAS and East . The software used provides results that are “close enough” for the cases that can be compared and considering that simulation results are being compared to formulaic methods. Th e reason this software is used instead of other more commonly available methods is because this software has the ability to simulate trials with a specific type of departure from proportional hazards, specifically the case where the target hazard ratio is not initially applicable but becomes applicable later in time. Neither SAS nor East has this capability, for example, and base their computations on assuming proportional hazards. This appendix subsequently discusses the details of the methodology implemented in the clinical trials simulation software used by BNIT. T he type of departure from proportional hazards can be generically identified as a later separation of Kaplan -Meier estimates, and there is conjecture that immunotherapies are subject to delayed effect consistent with what is seen when Kaplan -Meier estimates have delayed separation. T he observation of delayed separation of the BNIT phase 2 trial survival Kaplan- Meier estimates was the reason the phase 3 trial was planned with delayed effect and therefore the simulation software implementing this type of departure from proportional haz ards was used. If the delayed effect conjecture was not used to plan the BNIT phase 3 trial then the trial could be seriously underpowered and the interim analysis plan could lead to meeting futility criterion earlier than valid. The clinical trials simulation software criteria generated for early detection of superiority also prevents inappropriate triggering of superiority stopping rules. T here is limited ability to validate the results from this software because the methodology for implementing delayed effect is relatively new. Delayed effect validation has been implemented in a variety of ways, including assessing whether the gradual implementation of delayed separation gives results consistent with expectations and comparison to some other implementations of delayed separation. For example, ECOG used a sum of exponentials survival distribution and numerical integration to plan some leukemia trials where delayed separation of Kaplan -Meier estimates was apparent. The R code implementing this numerical integration trial planning methodology was provided and the clinical trials simulation software results implementing the intended delay were compared and the results were favorable within the limits of the fundamental differences in methods. The results from the clinical trial simulation software ha ve also been compared with the same comparability conclusion to software implementing a discrete chain Markov method as described by Shih [ Shih JH. Sample size calculation for complex clinical trials with survival endpoints. Controlled Clinical Trials 1995; 16:395-407.]. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 27 Revised: June 2015 Following is a description of the simulation methodology used to evaluate the statistical operating characteristics of trials where the intervention under study is subject to a delay of effect. (The text that follows is based on a methodological manuscript submitted for publication.) T he reasons for a delayed effect include: (1) Randomization of subjects with advanced disease for which experimental intervention is “too late”. (2) The experimental in tervention has a delayed onset of benefit. One or both of these mechanisms could apply. Assume a two -arm randomized clinical trial and that individuals entering this trial are to be randomized to one of two groups designated as arms 1 and 2, where arm 1 is to receive control intervention (perhaps a placebo) and arm 2 is to receive experimental intervention. Assume that the outcome of interest is time to a failure event or time to the last date when this event has yet to be observed. Assume it is hypothesized that the experimental intervention induces a distribution of event times that is more favorable, that is, shifted to the right, as compared to the distribution of event times associated with the control intervention. Assume competing events are either nonexistent or negligible. T rials of this type are typically planned by computing the required number of events under the assumption of proportional hazard rates, specific type I and II error probabilities, and a hypothesized clinically consequential ratio of the arm 2 (experimental) hazard rate to the arm 1 (control) hazard rate. Let h A be the hypothesized consequential hazard ratio. L et the hazard ratio function h(t) = λ1(t)/ λ 2(t) where λ 1(t) and λ 2(t) are hazard rate functions for arm 1 and 2, respectively. The proportional hazard rates assumption means that h(t) = λ 1(t)/ λ 2(t) = h (a constant). A fter computing the required number of events, projections for the timing of analyses are usually made based on assuming exponential distributions for the arms with hazard rates λ1(t) = λ1 and λ2(t) = λ 2 = h Aλ1. Thus, when there is no delayed effect the survival functions for arm 1 and arm 2 are usually specified to be F1(T ≤ t) = exp( -λ1t) F2(T ≤ t) = exp( -λ2t) = exp( -hAλ1t). T he delayed effect simulation meth odology described here is based on assuming that the control arm survival distribution is exponential. (It is possible to generalize this methodology to other types of control arm survival distributions though the use of the exponential distribution is usual.) N ow, assume that the experimental intervention has a lag in realization of effect so that the arm 2 hazard rate is initially λ1 and only later becomes h Aλ1. This delay of effect assumes the arm 2 hazard rate function is λ2(t) = ( λ1 - λ2)g(t) + λ2, (t = 0), w here g(t) is a function with the following properties: g(0) = 1, g(t) is non- increasing, and either g(t) = 0 for some t’ where t ≥ t’ > 0 or g(t)  0 as t  ∞. This implementation of delayed effect specifies that ultimately the hazard ratio becomes proportional or approaches proportionality. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 28 Revised: June 2015 I n order to do simulations under delayed effect the survival function F 2(t) must be derived. This survival function is obtained using the relationship F2(t) = exp( -H2(t)) w here H2(t) is the integral of λ 2(t) from 0 to t (integrated hazard function). Depending on the function g(t) the integral H 2(t) may have an analytic form; if not then numerical integration can be used. A segmented linear form of g(t) will now be described. (Other forms of g(t) are possible.) The segmented linear form of g(t) has two parameters, d and r, where d ≥ 0 and r > 0. The parameter d is the delay in the start of the effect of the experimental intervention and r is the time it takes to realize full effect from the start of realiza tion of effect. The function g(t) is specified linear and descending between d and d + r.. Specifically, g(t) is defined as follows g (t) = 1 for t < d g(t) = (d + r – t)/r for d ≤ t < d + r, g(t) = 0 for t ≥ d + r. It f ollows that H2(t) = (λ 1 - λ2)t + λ 2t = λ1t for t < d H2(t) = (λ 1 - λ2){d + [(d + r)(t – d) – (t2 – d2)/2]/r} + λ 2t for d ≤ t < d + r, H2(t) = (λ 1 - λ2)(d + r/2) + λ2t for t ≥ d + r. P seudo random survival times from F 2 can be generated by tabling its inverse and using a uniform pseudo random generator. T hus, if F 1 is exponential (as usual) and F 2 is defined as above then pseudo random samples of survival times from F 1 and F 2 can be generated for specified values of λ 1, λ2, d and r. These survival times can be converted to clinical trial data based on specification of accrual distribution and follow-up intervals. T he consequence of delayed effect is a reduction in projected statistical power relative to no delayed effect. Specifically, events occurring prior to the time of realization of the specified effect h A will be less differentiating than events that occur after full realization of the specified effect h A, and therefore only the events occurring after the time of realization of effect fully contribute to statistical power. Thus, if a trial is planned without taking into account delayed effect then the projected power of the trial will be overestimated if delayed effect is present in the data. S tatistical operating characteristics for a specific set of trial specifications, including parameters specifying a delayed effect, are estimated from replicating the generation trial data for the control arm and the experimental arm, that is, from replicates of the simulated trial data. Thus, for specified significance criteria the statistical operating characteristics of the test of the null Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 29 Revised: June 2015 hypothesis for superiority of the experimental intervention can be estimated using the logrank test on the simulated trial replicates. The following statistical operating characteristic estimates are most often of interest: •On e-sided type 1 error probability: The proportion of replicates for which the test of the null hypothesis is rejected using the one-sided logrank test and when the experimental arm data are generated with λ 2 = λ 1. •Power: The proportion of replicates for which the test of the null hypothesis is rejected using the one-sided logrank test and when the experimental arm data are generated with λ2 = h Aλ1. T hese estimates also can be made for interim analyses based on event time or calendar time, and the cumulative estimates (trial -wise) are those that take into whether and when specific trial replicates meet interim analysis criteria. T he statistical operating characteristics of futility tests can also be estimated using simulation. A common approach to futility testing is based on specifying an additional parameter Ω , usually Ω is the hazard ratio specified in the specific superiority alternative hypothesis, and then testing as the futility null hypothesis that λ 2 = Ωλ 1 against the futility alternative hypothesis that λ 2 > Ωλ 1. Evidence against the futility null hypothesis, that is, rejection of the futility null hypothesis, provides a basis for concluding that futility exists. The futility null hypothesis can be tested using a modification of the logrank test or proportional hazard regression where an offset corresponding to Ω is used to set up the futility null hypothesis. T he futility testing scheme described is applicable only when the proportional hazards assumption is tenable, however. Specifically, when delayed effect is applicable then the futility null hypothesis survival distributions will not have proportional hazards. The consequence will be an increase in the likelihood of falsely concluding futility because of the interaction between delayed effect and the offset correction in the statistical test. T herefore a different approach must be used for futility testing when delaye d effect is applicable. The chosen method is to modify the logrank test by using zero weights for event times prior to a pre-specified time T eff, a time specified to be after full effect or nearly full effect is applicable. Specifically, the two -sample logrank statistic is one of a class of linear rank statistics that can be written as Σw i(di2 – n i2di/ni) w here the sum is over each distinct time t i at which one or more events occur, d i2 is the number of arm 2 events at t i, ni2 is the number at risk at t i, di is the total number of deaths at t i, ni is the total number at risk at t i, and w i is a weight. For example, for the logrank test w i = 1 for all i, and for the Wilcoxon test w i = n i. The modified logrank statistic therefore has w i = 0 for all t i < T eff, and w i = 1 otherwise. The effect is to focus the futility testing to times after the delay of effect so that the likelihood of falsely concluding futility is controlled but with a reduction in sensitivity for futility. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 30 Revised: June 2015 The operating characteristics of the futility testing can be assessed by generating experimental arm data replicates using F 1 and F 2 (given previously). The logrank test with the offset Ω and modifications of weights using T eff as specified can then be used to obtain the following estimates statistical operating characteristics for futility testing: •F alse futility probability (futility test type I error probability): The proportion of replicates for which the test of the futility null hypothesis is rejected for a specified level of significance. The futility testing will be valid if this proportion is close to thesignificance level used in the testing of the futility null hypothesis. •Probability of futility conclusion conditional on specifications of truth: The proportion ofreplicates for which the test of the futility null hypothesis is rejected. Various values of truth (h A in the specification of F 2) can be used to assess the likelihood of concluding futility over a range of versions of truth. A close equivalent to the logrank test described above for futility testing (with an offset and weighting adjusted to T eff) can be implemented as a proportional hazard regression model with a time-dependent covariate for arm. This model has the arm variable as zero prior to time T eff and zero or on e for times equal or greater than T eff for the control arm and experimental arms, respectively. Bavarian Nordic, Inc. Statistical Analysis Plan Amendment 1 Protocol: BNIT -PRV -301 Date Issued: 29 June 2015 C onfidential 32 Revised: June 2015 16.Appendix C: Imputation Algorithm for Partial and Missing Dates Adverse Event If onset date is completely missing, onset date is set to date of first dose. I f year is present and month and day are missing: If year = year of first dose, then set month and day to month and day of first dose.