ArticleLifetime data analysis2019
Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression.
Article in Lifetime data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Neural network on interval-censored data with application to the prediction of Alzheimer's disease.Biometrics · 2023Article
- An information ratio-based goodness-of-fit test for copula models on censored data.Biometrics · 2023Article
- Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly.Statistical methods in medical research · 2023Article
- Genome-wide association study-based deep learning for survival prediction.Statistics in medicine · 2020Article
- Gene-based association analysis for bivariate time-to-event data through functional regression with copula models.Biometrics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Motivated by a genome-wide association study to discover risk variants for the progression of Age-related Macular Degeneration (AMD), we develop a computationally efficient copula-based score test, in which the dependence between bivariate progression times is taken into account. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and observed information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards assumption are considered. Extensive simulation studies are conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method to a large randomized trial data, the Age-related Eye Disease Study, to identify susceptible risk variants for AMD progression. The top variants identified on Chromosome 10 show significantly differential progression profiles for different genetic groups, which are critical in characterizing and predicting the risk of progression-to-late-AMD for patients with mild to moderate AMD.
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