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Cumulative risk regression in case–cohort studies using pseudo-observations

Author

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  • Erik T. Parner

    (Aarhus University)

  • Per K. Andersen

    (University of Copenhagen)

  • Morten Overgaard

    (Aarhus University)

Abstract

Case–cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case–cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case–cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case–cohort survival data. We adjust for the case–cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

Suggested Citation

  • Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09492-3
    DOI: 10.1007/s10985-020-09492-3
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    References listed on IDEAS

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    1. Langholz, Bryan & Jiao, Jenny, 2007. "Computational methods for case-cohort studies," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3737-3748, May.
    2. Zhang, Haimeng & Goldstein, Larry, 2003. "Information and asymptotic efficiency of the case-cohort sampling design in Cox's regression model," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 292-317, May.
    3. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    4. Jianwen Cai & Donglin Zeng, 2007. "Power Calculation for Case–Cohort Studies with Nonrare Events," Biometrics, The International Biometric Society, vol. 63(4), pages 1288-1295, December.
    5. Michal Kulich & D.Y. Lin, 2004. "Improving the Efficiency of Relative-Risk Estimation in Case-Cohort Studies," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 832-844, January.
    6. S. Kang & J. Cai, 2009. "Marginal hazards model for case-cohort studies with multiple disease outcomes," Biometrika, Biometrika Trust, vol. 96(4), pages 887-901.
    7. Morten Overgaard & Erik Thorlund Parner & Jan Pedersen, 2018. "Estimating the variance in a pseudo‐observation scheme with competing risks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(4), pages 923-940, December.
    8. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    9. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    10. Martin Jacobsen & Torben Martinussen, 2016. "A Note on the Large Sample Properties of Estimators Based on Generalized Linear Models for Correlated Pseudo-observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 845-862, September.
    11. Kani Chen, 2001. "Generalized case–cohort sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 791-809.
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    Cited by:

    1. Yayun Xu & Soyoung Kim & Mei-Jie Zhang & David Couper & Kwang Woo Ahn, 2022. "Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 241-262, April.

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