Choice of time scale for analysis of recurrent events data.

IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-08-15 DOI:10.1007/s10985-022-09569-1
Philip Hougaard
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引用次数: 0

Abstract

Recurrent events refer to events that over time can occur several times for each individual. Full use of such data in a clinical trial requires a method that addresses the dependence between events. For modelling this dependence, there are two time scales to consider, namely time since start of the study (running time) or time since most recent event (gap time). In the multi-state setup, it is possible to estimate parameters also in the case, where the hazard model allows for an effect of both time scales, making this an extremely flexible approach. However, for summarizing the effect of a treatment in a transparent and informative way, the choice of time scale and model requires much more care. This paper discusses these choices both from a theoretical and practical point of view. This is supported by a simulation study showing that in a frailty model with assumptions covered by both time scales, the gap time approach may give misleading results. A literature dataset is used for illustrating the issues.

Abstract Image

重复事件数据分析的时间尺度选择。
复发性事件是指随着时间的推移,每个人都可能发生多次的事件。在临床试验中充分利用这些数据需要一种处理事件之间依赖关系的方法。为了对这种依赖性进行建模,需要考虑两个时间尺度,即自研究开始以来的时间(运行时间)或自最近事件以来的时间(间隔时间)。在多状态设置中,也可以在风险模型考虑两个时间尺度影响的情况下估计参数,使其成为一种非常灵活的方法。然而,为了透明和信息地总结治疗效果,时间尺度和模型的选择需要更多的注意。本文从理论和实践两个角度对这些选择进行了探讨。一项模拟研究支持了这一点,该研究表明,在具有两种时间尺度的假设的脆弱性模型中,间隙时间方法可能会给出误导性的结果。文献数据集用于说明这些问题。
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
Lifetime Data Analysis
Lifetime Data Analysis 数学-数学跨学科应用
CiteScore
2.30
自引率
7.70%
发文量
43
审稿时长
3 months
期刊介绍: The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
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