Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression

Sun, Tao; Liu, Yi; Cook, Richard J.; Chen, Wei; Ding, Ying

doi:10.1007/s10985-018-09459-5

Cited by 19 publications

(12 citation statements)

References 33 publications

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“…The overall performance is similar to the all variants scenario. The inflation in Cox-Rst is slightly more compared to the all variants scenario, which is also consistent with the observation in Sun et al (2019).…”

Section: Type I Errorsupporting

confidence: 89%

“…Similar observations were found in Sun et al . () where they consider fitting copula models to test a single covariate (eg, SNP) with bivariate censored outcomes. Therefore, as in all parametric analysis, the model needs to be correctly specified.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…With robust variance‐covariance estimates, Cox‐Rst still shows some inflation in type I error, especially under small

α

tail. Similar observations were found in testing single variants with bivariate survival outcomes (Sun et al ., ). Within the copula‐based FLM, the correct Clayton model produces satisfactory‐controlled type I errors while the Gumbel model has shown some inflation due to the misspecified correlation structure.…”

Section: Simulation Studymentioning

confidence: 97%

“…The inflation in Cox‐Rst is slightly more compared to the all variants scenario, which is also consistent with the observation in Sun et al . ().…”

Section: Simulation Studymentioning

confidence: 97%

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Gene‐based association analysis for bivariate time‐to‐event data through functional regression with copula models

et al. 2019

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Several gene‐based association tests for time‐to‐event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene‐based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age‐related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age‐related Eye Disease Study, and to identify the gene regions that are associated with AMD progression.

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Section: Type I Errorsupporting

confidence: 89%

Section: Conclusion and Discussionmentioning

confidence: 99%

“…With robust variance‐covariance estimates, Cox‐Rst still shows some inflation in type I error, especially under small

α

Section: Simulation Studymentioning

confidence: 97%

“…The inflation in Cox‐Rst is slightly more compared to the all variants scenario, which is also consistent with the observation in Sun et al . ().…”

Section: Simulation Studymentioning

confidence: 97%

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Gene‐based association analysis for bivariate time‐to‐event data through functional regression with copula models

et al. 2019

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“…Ideally one should take the correlation into account when constructing the prediction model. One possible extension includes using a copula model to account for the dependence between the two eyes from the same subject 48,49 and predicting the joint progression profiles of the two eyes through a DNN. We are investigating some of these extensions.…”

Section: Ta B L Ementioning

confidence: 99%

Genome‐wide association study‐based deep learning for survival prediction

Sun

Yue

Chen

et al. 2020

Statistics in Medicine

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Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome‐wide association studies (GWAS), together with well‐characterized time‐to‐event phenotypes provide unprecedented opportunities for developing effective survival prediction models. Recent advances in deep learning have made extraordinary achievements in establishing powerful prediction models in the biomedical field. However, the applications of deep learning approaches in survival prediction are limited, especially with utilizing the wealthy GWAS data. Motivated by developing powerful prediction models for the progression of an eye disease, age‐related macular degeneration (AMD), we develop and implement a multilayer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Various simulation studies are performed to compare the prediction performance of the DNN survival model with several other machine learning‐based survival models. Finally, using the GWAS data from two large‐scale randomized clinical trials in AMD with over 7800 observations, we show that the DNN survival model not only outperforms several existing survival prediction models in terms of prediction accuracy (eg, c‐index =0.76), but also successfully detects clinically meaningful risk subgroups by effectively learning the complex structures among genetic variants. Moreover, we obtain a subject‐specific importance measure for each predictor from the DNN survival model, which provides valuable insights into the personalized early prevention and clinical management for this disease.

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