使用预期竞争风险数据对特定原因危害进行回归建模的标准方法为每种故障类型指定了单独的模型。 Lunn 和 McNeil (1995) 提出的另一种方案假设特定原因的危害与各种原因成比例。这可能比标准方法更有效，并且可以比较不同原因的协变量效应。在本文中，我们将 Lunn 和 McNeil（1995）扩展到嵌套病例对照研究，适应具有额外匹配和非比例性的场景。我们还考虑了从同一队列中进行的不同研究获得不同原因数据的情况。事实证明，虽然在完整的队列分析中只能实现适度的效率提升，但对于相对罕见的失败类型，嵌套病例对照分析可能会获得显着的收益。使用前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验 (PLCO) 研究进行了广泛的模拟研究，并提供了真实数据分析。© 作者 2024。由牛津大学出版社出版。版权所有。 [br]如需权限，请发送电子邮件至：journals.permissions@oup.com。

The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study.© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.