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Goodness-of-fit inference for the additive hazards regression model with clustered current status data.

ABSTRACT: Clustered current status data are frequently encountered in biomedical research and other areas that require survival analysis. This paper proposes graphical and formal model assessment procedures to evaluate the goodness of fit of the additive hazards model to clustered current status data. The test statistics proposed are based on sums of martingale-based residuals. Relevant asymptotic properties are established, and empirical distributions of the test statistics can be simulated utilizing Gaussian multipliers. Extensive simulation studies confirmed that the proposed test procedures work well for practical scenarios. This proposed method applies when failure times within the same cluster are correlated, and in particular, when cluster sizes can be informative about intra-cluster correlations. The method is applied to analyze clustered current status data from a lung tumorigenicity study.

SUBMITTER: Feng Y

PROVIDER: S-EPMC10291921 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Goodness-of-fit inference for the additive hazards regression model with clustered current status data.

Feng Yanqin Y Wang Jie J Li Yang Y

Journal of applied statistics 20220324 9

Clustered current status data are frequently encountered in biomedical research and other areas that require survival analysis. This paper proposes graphical and formal model assessment procedures to evaluate the goodness of fit of the additive hazards model to clustered current status data. The test statistics proposed are based on sums of martingale-based residuals. Relevant asymptotic properties are established, and empirical distributions of the test statistics can be simulated utilizing Gau ...[more]

PMID: 37378268

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