Cook Richard J, Lawless Jerald F
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.
Biostatistics. 2024 Dec 31;26(1). doi: 10.1093/biostatistics/kxae039.
In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.
在队列研究数据的生命历程分析中,处理参与者的识别和选择过程很重要。许多健康研究根据个体是否经历过某些与健康相关的事件来选择或招募个体,例如疾病诊断或疾病的某些并发症。标准分析方法依赖于关于选择与个体预期生命历程过程独立性的假设,前提是已知其既往病史。然而,违反此类假设的情况很常见,并且可能会使过程特征的估计产生偏差。这对队列研究的内部和外部有效性以及结果向总体的可转移性都有影响。在本文中,我们通过为队列选择过程和感兴趣的失败过程提出一个联合模型来研究生存时间分析。这使我们能够处理独立性假设以及研究结果的可转移性。结果表明,在缺乏总体辅助信息的情况下无法保证可转移性。在数值研究中讨论并举例说明了产生相关选择的条件和辅助数据的类型。所提出的框架应用于一项关于银屑病患者患银屑病关节炎风险的研究。