Interactions will also be explored for contribution to the model. 8.2.3 Analysis of Secondary Efficacy Variables The secondary efficacy endpoints of TTNT, DMFS, ORR and QoL using the EQ -5D-5L or EQ - 5D-Y will be compared between treatment arms . The secondary efficacy endpoints will only be evaluated if at least one of the primary efficacy endpoints (PFS/OS) demonstrates superiority for CMB305 over placebo. Furthermore, to control the overall family -wise type I error rate at 1 -sided α = 0.025 for the secondary efficacy endpoints, the secondary efficacy endpoint of TTNT will be tested first at 1- sided alpha of 0.025. DMFS will be tested at 1 -sided alpha of 0.025 only if TTNT shows significant improvement. 8.2.3.1 Time to Next Treatment (TTNT) TTNT will be a nalyzed using the same methods described for OS/PFS. In the event that the percent of subjects with competing events such as death, los t to follow -up and withdraw al of consent in either arm exceeds 3%, the cumulative incidence estimates and Gray’s t est will be the main TTNT comparison between the treatment arms. 05FPRF Immune Design Corp. I MDZ -04-1702 SAP Draft V1.0 for Protocol V2.0 Version Date: 2018- 05-25 Confidential 31 / 36 Example code for cumulative incidence estimates and Gray’s test : data risk; Baseline=1 ; trt=1; output; trt=2; output; format trt trtgroup.; run; proc phreg data=adtte (where=(paramcd='TTNT')) plots(overlay=strata)=cif; class trt / param=glm order=internal ref=first; model aval*Status(0 ) = trt baseline / eventcode=1 ; hazardratio 'Subdistribution Hazards' Group / diff=pairwise; baseline covariates=risk out=_null_ / rowid=trt; run; 8.2.3.2 Distant Metastasis -Free Survival (DMFS) DMFS will be analyzed using the same methods described for OS/PFS. In the event that the percent of subjects with competing events such as death, los t to follow -up and withdraw al of consent in either arm exceeds 3%, the cumulative incidence estimates and G ray’s test will be the main DMFS comparison between the treatment arms. 8.2.3.3 Objective Response Rate (ORR) ORR will be estimated using the ITT set and EE set by both investigator’s and IRC response assessment , the percent of subjects will be provided along with their corresponding 2- sided the 95% exact CI using Clopper -Pearson method. Also the best overall tumor response (CR, PR, SD, PD and not evaluable) will be tabulated with 2- sided 95% CI in the same table . Also, ORR will be compared between treatment arms using a logistic regression. Logistic regression will be performed with the treatment arm and all stratification factors as independent variables. Treatment arm will be compared using the li kelihood ratio chi -square test. 8.2.3.4 QoL using the EQ -5D-5L or EQ -5D-Y QOL analysis will be performed based on the ITT and EE set . For EQ -5D-5L, number of subject and percentage will be provided by visit for each dimensions ; observation, change and % change from baseline will be summarized for EQ -5D index value and VAS by each scheduled visit per treatment arm and overall. Also, t he treatment difference between CMB305 to placebo in the mean and change of the EQ - 5D-5L index score/ VAS from baseline to each post baseline analysis visit will be estimated using pattern mixture models . The following covariates will be considered: randomization stratification factors , baseline value of index score/ VAS , treatment arm , analysis visit, anal ysis visit * treatment arm. Both index score/VAS values and change from baseline of each analysis visit will be included as dependent variable separately. 05FPRF Immune Design Corp. I MDZ -04-1702 SAP Draft V1.0 for Protocol V2.0 Version Date: 2018- 05-25 Confidential 32 / 36 EQ-5D-Y will be analyzed using the same methods described for EQ -5D-5L, except for index score (no in dex score will be calculated for EQ -5D-Y). Example code for mixed model for repeated measures for change fr om baseline of index score/VAS: proc mixed data = data.qol method=REML; class subjid trt avisit stratum1 stratum2 stratum3; model socre_change = baseline stratum1 stratum2 stratum3 trt avisit trt*avisit /NOINT SOLUTION DDFM=SAT; repeated avisit/ type = UN subject = subjid r ; Lsmeans trt*avisit/diff CL; run; In the case of non- convergence, the covariance structure type and computation method will be adjusted until convergence achieves without errors. 8.2.4 Analysis of Exploratory Efficacy Variables The baseline anti -NY-ESO -1 immune response and histologic and molecular changes in peripheral blood and tumor tissue and their association with clinical outcomes will be explored.