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Marginal hazards model for case-cohort studies with multiple disease outcomes


  • S. Kang
  • J. Cai


Case-cohort study designs are widely used to reduce the cost of large cohort studies while achieving the same goals, especially when the disease rate is low. A key advantage of the case-cohort study design is its capacity to use the same subcohort for several diseases or for several subtypes of disease. In order to compare the effect of a risk factor on different types of diseases, times to different events need to be modelled simultaneously. Valid statistical methods that take the correlations among the outcomes from the same subject into account need to be developed. To this end, we consider marginal proportional hazards regression models for case-cohort studies with multiple disease outcomes. We also consider generalized case-cohort designs that do not require sampling all the cases, which is more realistic for multiple disease outcomes. We propose an estimating equation approach for parameter estimation with two different types of weights. Consistency and asymptotic normality of the proposed estimators are established. Large sample approximation works well in small samples in simulation studies. The proposed methods are applied to the Busselton Health Study. Copyright 2009, Oxford University Press.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:biomet:v:96:y:2009:i:4:p:887-901

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    3. Jieli Ding & Tsui-Shan Lu & Jianwen Cai & Haibo Zhou, 2017. "Recent progresses in outcome-dependent sampling with failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 57-82, January.
    4. repec:eee:csdana:v:117:y:2018:i:c:p:194-206 is not listed on IDEAS
    5. repec:bla:biomet:v:72:y:2016:i:4:p:1037-1045 is not listed on IDEAS

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