In hospital epidemiology, logistic regression is a popular model to study risk factors of hospital‐acquired infections. One key issue in this analysis is how to incorporate the time dependency of acquiring an infection during the hospital stay. In the applied literature, researchers often simply adjust for the entire length of hospital stay, which also includes the time after infection. A further issue is that discharge and death are competing events for hospital‐acquired infections. After discussing the limitations of logistic regression adjusted for length of stay in this setting, we compare this approach with appropriate analyses incorporating competing risks and with an illness–death model with hospital‐acquired infection as an intermediate event. The cumulative incidence function, cause‐specific hazard ratios, and subdistribution hazard ratios are considered as reference measures. Real‐life and simulated data are used to demonstrate biases and limitations associated with logistic regression adjusted for length of stay. We conclude that logistic regression adjusted for length of stay should not be used when investigating hospital‐acquired infections and that appropriate methods involving the use of multistate models should be used to capture the time dependency in time‐to‐event settings, especially in the presence of competing events.

中文翻译：

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在临床流行病学回归模型中考虑住院时间

在医院流行病学中，逻辑回归是研究医院获得性感染危险因素的流行模型。该分析中的一个关键问题是如何结合住院期间感染的时间依赖性。在应用文献中，研究人员经常简单地调整整个住院时间，其中还包括感染后的时间。另一个问题是出院和死亡是医院获得性感染的竞争事件。在讨论了在这种情况下针对住院时间调整的逻辑回归的局限性之后，我们将该方法与纳入竞争风险的适当分析以及以医院获得性感染为中间事件的疾病死亡模型进行了比较。累积发生率函数，特定原因的危险比，和子分布危害比被认为是参考措施。现实生活和模拟数据用于证明与逻辑回归相关的偏倚和局限性，并根据住院时间进行了调整。我们得出的结论是，在调查医院获得性感染时，不应使用针对住院时间进行调整的逻辑回归，并且应使用涉及使用多状态模型的适当方法来捕获事件发生时间设置中的时间依赖性，尤其是在存在情况下比赛项目。