ArticleBMC bioinformatics2024
AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- SurvGME: an R package for survival analysis with graphical and measurement error models.BMC bioinformatics · 2026Article
- Biomarker-assisted reporting in nutritional epidemiology: addressing measurement error in exposure-disease associations.Biostatistics (Oxford, England) · 2025Article
- Two-stage sampling for better survival model performance.BMC medical research methodology · 2025Article
- Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization.BMC cancer · 2025Article
- Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data.BMC medical research methodology · 2025Article
- AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data.BMC bioinformatics · 2024Article
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Authors and funding
2 authors.
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Abstract
backgroundSurvival analysis has been used to characterize the time-to-event data. In medical studies, a typical application is to analyze the survival time of specific cancers by using high-dimensional gene expressions. The main challenges include the involvement of non-informaive gene expressions and possibly nonlinear relationship between survival time and gene expressions. Moreover, due to possibly imprecise data collection or wrong record, measurement error might be ubiquitous in the survival time and its censoring status. Ignoring measurement error effects may incur biased estimator and wrong conclusion.
resultsTo tackle those challenges and derive a reliable estimation with efficiently computational implementation, we develop the R package AFFECT, which is referred to Accelerated Functional Failure time model with Error-Contaminated survival Times.
conclusionsThis package aims to correct for measurement error effects in survival times and implements a boosting algorithm under corrected data to determine informative gene expressions as well as derive the corresponding nonlinear functions.
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