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Analysis of multiple survival events in generalized case‐cohort designs

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  • Soyoung Kim
  • Donglin Zeng
  • Jianwen Cai

Abstract

Generalized case‐cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data. Furthermore, we examine the optimal choice of weights and show that this optimal weighting yields much improved efficiency gain both asymptotically and in simulation studies. Finally, we apply our proposed methods to data from the Atherosclerosis Risk in Communities study.

Suggested Citation

  • Soyoung Kim & Donglin Zeng & Jianwen Cai, 2018. "Analysis of multiple survival events in generalized case‐cohort designs," Biometrics, The International Biometric Society, vol. 74(4), pages 1250-1260, December.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:4:p:1250-1260
    DOI: 10.1111/biom.12923
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    References listed on IDEAS

    as
    1. S. Kim & J. Cai & W. Lu, 2013. "More efficient estimators for case-cohort studies," Biometrika, Biometrika Trust, vol. 100(3), pages 695-708.
    2. 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.
    3. 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.
    4. 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.
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    Cited by:

    1. Wei Wang & Shou‐En Lu & Jerry Q. Cheng & Minge Xie & John B. Kostis, 2022. "Multivariate survival analysis in big data: A divide‐and‐combine approach," Biometrics, The International Biometric Society, vol. 78(3), pages 852-866, September.
    2. Soyoung Kim & Yayun Xu & Mei‐Jie Zhang & Kwang‐Woo Ahn, 2020. "Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1222-1242, December.

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