Statistical issues in survival analysis (adaptive Lasso for Cox model)
September 10, 2026 The authors proposed use of an outcome-adaptive Lasso (OAL) Cox proportional hazards model as a novel variable selection framework causal inference for right censored data in light of a critical limitation in survival analysis where propensity score modeling mitigates confounding bias in observational data but is highly sensitive to covariate selection. Their proposed method integrates the OAL with the Cox proportional hazards model by constructing penalty weights from coefficients estimated through the Cox partial likelihood. This design enabled OAL-Cox to effectively identify outcome-related confounders while excluding irrelevant covariates, including instrumental and spurious variables, even under censoring and correlated covariate structures. They found their OAL-Cox was more effective at reducing the inclusion of instrumental and spurious covariates than L-Cox and it also yieled more stable restricted mean survival time-based