Selection processes, transportability, and failure time analysis in life history studies.

IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biostatistics Pub Date : 2024-10-27 DOI:10.1093/biostatistics/kxae039
Richard J Cook, Jerald F Lawless
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引用次数: 0

Abstract

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.

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生命史研究中的选择过程、可迁移性和失效时间分析。
在对队列研究的数据进行生命史分析时,重要的是要解决参与者的识别和选择过程。许多健康研究都是根据个人是否经历过某些健康相关事件(如疾病诊断或疾病引起的某些并发症)来选择或招募个人的。标准的分析方法依赖于对选择的独立性和一个人的前瞻性生活史过程(考虑到其先前的历史)的假设。然而,违反这些假设的情况很常见,而且会对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在人群中的可迁移性都有影响。在本文中,我们通过提出队列选择过程和相关失效过程的联合模型来研究失效时间分析。这样,我们就能同时解决独立性假设和研究结果的可迁移性问题。研究表明,在没有人口辅助信息的情况下,可迁移性无法得到保证。在数值研究中讨论并说明了产生依赖性选择的条件和辅助数据类型。提出的框架适用于银屑病患者银屑病关节炎风险的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
期刊最新文献
Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms. The impact of coarsening an exposure on partial identifiability in instrumental variable settings. Selection processes, transportability, and failure time analysis in life history studies. Functional quantile principal component analysis. Shared parameter modeling of longitudinal data allowing for possibly informative visiting process and terminal event.
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