Methodological challenges in studying disease processes using observational cohort data.

IF 1 Q3 STATISTICS & PROBABILITY
Richard J Cook, Jerald F Lawless
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引用次数: 0

Abstract

Cohort studies of disease processes deal with events and other outcomes that may occur in individuals following disease onset. The particular goals are often the evaluation of interventions and estimation of the effects of risk factors that may affect the disease course. Models and methods of event history analysis and longitudinal data analysis provide tools for understanding disease processes, but there are numerous challenges in practice. These are related to the complexity of the disease processes and to the difficulty of recruiting representative individuals and acquiring detailed longitudinal data on their disease course. Our objectives here are to describe some of these challenges and to review methods of addressing them. We emphasize the appeal of multistate models as a framework for understanding both disease processes and the processes governing recruitment of individuals for cohort studies and the collection of data. The use of other observational data sources in order to enhance model fitting and analysis is discussed.

使用观察性队列数据研究疾病过程的方法学挑战。
疾病过程的队列研究处理疾病发病后个体可能发生的事件和其他结果。具体目标通常是评估干预措施和估计可能影响疾病进程的风险因素的影响。事件历史分析和纵向数据分析的模型和方法为了解疾病过程提供了工具,但在实践中存在许多挑战。这与疾病过程的复杂性以及招募具有代表性的个体和获取其疾病过程的详细纵向数据的难度有关。我们在这里的目标是描述其中的一些挑战,并回顾解决这些挑战的方法。我们强调多状态模型作为理解疾病过程和管理队列研究和数据收集的个体招募过程的框架的吸引力。讨论了利用其他观测数据源来加强模型拟合和分析。
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
CiteScore
2.00
自引率
15.40%
发文量
42
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