利用嵌套病例对照研究的比例风险模型进行控制合并分析
Pooling controls from nested case-control studies with the proportional risks model
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影响因子:2
分区:数学3区 / 数学与计算生物学2区 统计学与概率论3区
发表日期:2024 Dec 31
作者:
Yen Chang, Anastasia Ivanova, Demetrius Albanes, Jason P Fine, Yei Eun Shin
DOI:
10.1093/biostatistics/kxae032
摘要
因果特异性风险的回归模型的标准方法针对具有前瞻性竞争风险数据的每种失败类型分别建立模型。Lunn 和 McNeil(1995)提出的替代方法假设不同原因的因果风险成比例。这可能比标准方法更高效,并允许比较不同原因之间的协变量效应。本文将Lunn 和 McNeil(1995)的方法扩展到嵌套病例对照研究,适用于具有额外匹配和非比例风险场景的情况。我们还考虑了从同一队列中不同研究获得不同原因数据的情况。结果显示,虽然在完整队列分析中效率提升有限,但在相对罕见的失败类型的嵌套病例对照分析中,效率提升显著。我们进行了大量模拟研究,并利用前列腺、肺、结直肠和卵巢癌筛查试验(PLCO)数据进行了实证分析。
Abstract
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.