Subgroup identification based on an accelerated failure time model combined with adaptive elastic net / 南方医科大学学报
Journal of Southern Medical University
;
(12): 1200-1206, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-773474
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Controlled clinical trial
/
Diagnostic study
Language:
Chinese
Journal:
Journal of Southern Medical University
Year:
2019
Type:
Article
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