# Cox proportional hazards model

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

The authors conducted a comprehensive neutral simulation comparison based on two reference datasets from randomized controlled trials, evaluating the Cox proportional hazards model and random survival forest for patient-specific survival probability prediction across multiple performance measures following TRIPOD recommendations, and found that conclusions based solely on the C index may not generalize to other aspects of predictive performance, that measures of overall performance may generally give more reasonable results, that the standard log-rank splitting rule for the random survival forest may be outperformed by alternative splitting rules particularly in nonproportional hazards settings, that the random survival forest performance suffered less in data with treatment-covariate inte

The authors conducted a comprehensive neutral simulation comparison based on two reference datasets from randomized controlled trials, evaluating the Cox proportional hazards model and random survival forest for patient-specific survival probability prediction across multiple performance measures following TRIPOD recommendations, and found that conclusions based solely on the C index may not generalize to other aspects of predictive performance, that measures of overall performance may generally give more reasonable results, that the standard log-rank splitting rule for the random survival forest may be outperformed by alternative splitting rules particularly in nonproportional hazards settings, that the random survival forest performance suffered less in data with treatment-covariate inte