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Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model

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  • Xinyuan Chen
  • Denise Esserman
  • Fan Li

Abstract

A population‐averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated failure time data subject to competing risks. This approach extends the population‐averaged additive hazards model by accommodating potentially dependent censoring due to competing events other than the event of interest. Assuming an independent working correlation structure, an estimating equations approach is outlined to estimate the regression coefficients and a new sandwich variance estimator is proposed. The proposed sandwich variance estimator accounts for both the correlations between failure times and between the censoring times, and is robust to misspecification of the unknown dependency structure within each cluster. We further develop goodness‐of‐fit tests to assess the adequacy of the additive structure of the subdistribution hazards for the overall model and each covariate. Simulation studies are conducted to investigate the performance of the proposed methods in finite samples. We illustrate our methods using data from the STrategies to Reduce Injuries and Develop confidence in Elders trial.

Suggested Citation

  • Xinyuan Chen & Denise Esserman & Fan Li, 2024. "Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 281-301, May.
    • Handle: RePEc:bla:stanee:v:78:y:2024:i:2:p:281-301
    DOI: 10.1111/stan.12317
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    References listed on IDEAS

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    1. Yin, Guosheng, 2007. "Model checking for additive hazards model with multivariate survival data," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1018-1032, May.
    2. Kyu Ha Lee & Francesca Dominici & Deborah Schrag & Sebastien Haneuse, 2016. "Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1075-1095, July.
    3. Peng He & Frank Eriksson & Thomas H. Scheike & Mei-Jie Zhang, 2016. "A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 103-122, March.
    4. Yu Shen & S. C. Cheng, 1999. "Confidence Bands for Cumulative Incidence Curves Under the Additive Risk Model," Biometrics, The International Biometric Society, vol. 55(4), pages 1093-1100, December.
    5. Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
    6. Yanqin Feng & Jie Wang & Yang Li, 2023. "Goodness-of-fit inference for the additive hazards regression model with clustered current status data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(9), pages 1921-1941, July.
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