Left censoring can occur with relative frequency when analysing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study, may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The objective of this paper is to propose a statistical method that performs successfully in these circumstances. Our proposal is based on the use of models with specific baseline hazard, imputing the number of prior episodes when unknown, with a stratified model depending on whether the individual had or had not previously been at risk, and the use of a frailty term. The performance is examined in different scenarios through a comprehensive simulation study.The proposed method achieves notable performance even when the percentage of subjects at risk before the beginning of the follow-up is very elevated, with biases that are often under 10\% and coverages of around 95\%, sometimes somewhat conservative. If the baseline hazard is constant, it seems to be that the ``Gap Time'' approach is better; if it is not constant, the ``Counting Process'' seems to be a better choice. Because of the lack of knowledge of the prior episodes that have been experienced by a part (or all) of subjects, the use of common baseline methods is not advised. Our proposal seems to perform acceptably in the majority of the scenarios proposed, becoming an interesting alternative in this context.
翻译:在分析流行病学研究、特别是观察研究的经常性事件时,可以相对频繁地进行左左审查。具体地说,将那些在有效启动群体研究之前就已面临风险的个人纳入群体研究,可能会导致对已经经历过的先前事件不知情,这很容易导致偏差和低效率的估计数。本文件的目的是提出一种在这些情况中成功发挥作用的统计方法。我们的建议基于使用具有具体基准危害的模型,根据未知的先前事件的数量,根据个人是否曾经面临风险,以及使用一个分层模型来估计先前事件的数量,取决于个人是否曾经面临风险,以及使用一个脆弱术语。通过全面模拟研究在不同情况下对业绩进行审查。拟议方法取得了显著的成绩,即使开始采取后续行动之前面临风险的主体的百分比非常高,而且往往低于10 ⁇,覆盖范围约为95 ⁇,有时有些保守。如果基准危害不变,那么似乎更适合“Gap time”的方法;如果不是固定的,“Countetinging Processing Processing Process practive principle of a propossial proposion of propossibal propossibal part,似乎 a view view