Subgroup identification based on an accelerated failure time model combined with adaptive elastic net.
10.12122/j.issn.1673-4254.2019.10.11
- Author:
Pei KANG
1
;
Jun XU
2
;
Fuqiang HUANG
1
;
Yingxin LIU
1
;
Shengli AN
1
Author Information
1. Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
2. Department of Economic Management, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
accelerated failure time model;
adaptive design;
adaptive elastic net;
change-point algorithm;
precision medicine
- From:
Journal of Southern Medical University
2019;39(10):1200-1206
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:We propose a strategy for identifying subgroups with the treatment effect from the survival data of a randomized clinical trial based on accelerated failure time (AFT) model.
METHODS:We applied adaptive elastic net to the AFT model (designated as the penalized model) and identified the candidate covariates based on covariate-treatment interactions. To classify the patient subgroups, we utilized a likelihood-based change-point algorithm to determine the threshold cutoff point. A two-stage adaptive design was adopted to verify if the treatment effect existed within the identified subgroups.
RESULTS:The penalized model with the main effect of the covariates considerably outperformed the univariate model without the main effect for the trial data with a small sample size, a high censoring rate, a small subgroup size, or a sample size that did not exceed the number of covariates; in other scenarios, the latter model showed better performances. Compared with the traditional design, the adaptive design improved the power for detecting the treatment effect where subgroup effect exists with a well-controlled type Ⅰ error.
CONCLUSIONS:The penalized AFT model with the main effect of the covariates has advantages in subgroup identification from the survival data of clinical trials. Compared with the traditional design, the two-stage adaptive design has better performance in evaluation of the treatment effect when a subgroup effect exists.
