Additional file 1: of Bayesian variable selection for parametric survival model with applications to cancer omics data

Table S1. TPR, FPR and FDR in variable selection with 50 replications (Weibull distribution). Table S2. TPR, FPR and FDR in variable selection with 50 replications (Gamma distribution). Table S3. Computational time (minutes) for application in simulation trials. Table S4. The estimated effects of 14 SNPs by SurvEMVS and classical Weibull regression. Table S5. Demographic and clinical characteristics of STAD patients. Table S6. The estimated effects of gene expression levels selected by SurvEMVS and Cox LASSO with their counterparts in low dimension scenario (i.e., Weibull regession and Cox model, respectively). Figure S1. Pseudocode for implementation of SurvEMVS. Figure S2. Averaged estimated effect (black vertical lines) for each marker over 50 replications under Scenario 2. Figure S3. Averaged estimated effect (black vertical lines) for each marker over 50 replications under Scenario 3. Figure S4. Averaged estimated effect (black vertical lines) for each marker over 50 replications under Scenario 4. Figure S5. Averaged estimated effect (black vertical lines) for each marker over 50 replications under Scenario 5. Figure S6 Averaged estimated effect (black vertical lines) for each marker over 50 replications under Scenario 6. Figure S7. MSE of parameter estimation and AUC of prognosis prediction for Scenarios 3 and 4. Figure S8. MSE of parameter estimation and AUC of prognosis prediction for Scenarios 5 and 6. Figure S9. Kaplan-Meier survival curve of patients with high, moderate, and low risk. (DOCX 9932 kb