Dataset Information

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

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.

SUBMITTER: Sun T

PROVIDER: S-EPMC6559827 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Sun Tao T Liu Yi Y Cook Richard J RJ Chen Wei W Ding Ying Y

Lifetime data analysis 20181217 3

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 t ...[more]

PMID: 30560439

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Project description:Cardiovascular outcome trials, among others, aim to assess the beneficial effects of a treatment on multiple event-time outcomes, such as the time to a myocardial infarction and the time to a stroke. The traditional approach is to conduct a simple analysis of a composite outcome defined as the time to the first component event using a logrank test or the Cox Proportional Hazards regression model. This ignores information from other component events after the first. The composite outcome analysis also treats all initial outcome events as equally important, for example, non-fatal myocardial infarction is as important as cardiovascular death.Herein, we describe the application of the Wei-Lachin multivariate one-sided (or one-directional) test to the analysis of multiple event-time outcomes. The test is based on the unweighted mean of the treatment group coefficients from individual Cox proportional hazards models fit to the outcomes, where the covariance of the set of coefficients is obtained from a partitioning of the information sandwich estimate. A weighted test is also described, weighing the outcomes by a scoring of their clinical importance. These and other methods are compared with application to the Prevention of Events with Angiotensin-Converting Enzyme Inhibition cardiovascular outcome study.The Wei-Lachin test provides an inference with strong control of the type 1 error probability on the difference between groups for the set of outcomes considered. However, it does not provide an inference on the individual components specifically with control of the overall type 1 error probability. By direct computation of relative efficiency and by simulation, we show that the power of the Wei-Lachin one-directional test can be greater than that of the traditional composite outcome analysis based on the time to the first observed component event.The Wei-Lachin multivariate one-directional test may be more powerful than the traditional analysis of a composite outcome defined as the time to the first component outcomes experienced by each subject.

Dataset Information

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

Publications

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

Similar Datasets

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