ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2019
[Subgroup identification based on an accelerated failure time model combined with adaptive elastic net].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- [A simulation study of the reliability and accuracy of Cox-TEL method for estimating hazard ratio and difference in proportions for long-term survival data containing cured patients].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- [Comparison of prediction ability of two extended Cox models in nonlinear survival data analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- [Subgroup identification based on accelerated failure time model combined with adaptive elastic net].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- [Application of conditional inference forest in time-to-event data analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe 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.
methodsWe 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.
resultsThe 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.
conclusionsThe 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.
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