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Progress in immunotherapy revolutionized the treatment landscape for advancedlung cancer, raising survival expectations beyond those that were historicallyanticipated with this disease. In the present study, we describe the methodsfor the adjustment of mixture parametric models of two populations for survivalanalysis in the presence of long survivors. A methodology is proposed inseveral five steps: first, it is proposed to use the multimodality test todecide the number of subpopulations to be considered in the model, second toadjust simple parametric survival models and mixture distribution models, toestimate the parameters and to select the best model fitted the data, finally,to test the hypotheses to compare the effectiveness of immunotherapies in thecontext of randomized clinical trials. The methodology is illustrated with datafrom a clinical trial that evaluates the effectiveness of the therapeuticvaccine CIMAvaxEGF vs the best supportive care for the treatment of advancedlung cancer. The mixture survival model allows estimating the presence of asubpopulation of long survivors that is 44% for vaccinated patients. Thedifferences between the treated and control group were significant in bothsubpopulations (population of short-term survival: p = 0.001, the population oflong-term survival: p = 0.0002). For cancer therapies, where a proportion ofpatients achieves long-term control of the disease, the heterogeneity of thepopulation must be taken into account. Mixture parametric models may be moresuitable to detect the effectiveness of immunotherapies compared to standardmodels.

Mixture survival models methodology: an application to cancer immunotherapy assessment in clinical trials