Subgroup identification based on accelerated failure time model combined with adaptive elastic net

Abstract

To solve the problem of identifying subgroup in a randomized clinical trial with respect to survival time, we present a strategy based on accelerated failure time model to identify the subgroup with an enhanced treatment effect. We fitted and compared univariate accelerated failure time (AFT) models and penalized AFT models regularized by adaptive elastic net to identify the candidate covariates. Based on these covariates, we utilized change-point algorithm to classify the patient subgroups. A two-stage adaptive design was adopted to verify the treatment effect in certain subgroups. Simulations were conducted across different scenarios to evaluate the performance of the models. As the correlation between covariates differed, the power of the models with an adaptive design was stable. In the two-stage adaptive design, the power of the models was the highest when the two significance levels (α1 and α2) were allocated to be 0.035 and 0.015, respectively. A better effect of the responder subgroup was associated with a higher power of the models. For a fixed sample size, the power decreased as the covariate number to sample size ratio increased, but the power showed a stable trend when the ratio was above 1. The univariate models showed different distribution patterns of the parameters for different survival time, while their distribution was relatively stable in the penalized AFT models. The correlation between the covariates does not affect the performance of univariate AFT models and penalized AFT models. (0.035, 0.015) can be used as a reference for the significance level of an adaptive design. For small differences in the treatment effect between the responder and the non-responder, the penalized AFT model including the main effect of covariate (Penalized, Eq_in) outperforms the univariate AFT model excluding the main effect of covariate (Univariate, Eq_ex). Univariate, Eq_ex performs better when the covariate number to sample size ratio is less than 1, but is outperformed by Penalized, Eq_in when the ratio is above 1. The parameter distribution of survival time has a greater impact on the univariate models but a smaller impact on the penalized models.