标题： 先验事件率比的偏差减少方法，应用于晚期癌症患者急诊科就诊率 显示英文标题

标题： Bias reduction method for prior event rate ratio, with application to emergency department visit rates in patients with advanced cancer

摘要： 目标：前期事件率比（PERR）是一种有前景的方法，用于控制观察性研究和现实世界证据研究中的混杂因素。其假设之一是结果事件的发生不会影响后续事件率，换句话说，即不存在“事件依赖性”。本研究提出了一个偏差减少方法，当这一假设被违反时进行评估和说明。研究设计与环境：我们提出了条件脆弱性方法，在存在事件依赖性的情况下实施PERR，并通过模拟评估其性能。我们通过一项关于新加坡晚期癌症患者急诊科就诊率和姑息治疗的研究展示了该方法的使用。结果：模拟结果显示，在存在负（正）事件依赖性的情况下，治疗效果的粗略PERR估计值偏向（远离）零值。所提出的方法成功减少了偏差，绝对相对偏差的中位数约为5%。动态随机截距建模揭示了晚期癌症患者急诊科就诊存在正向事件依赖性。虽然常规的时间到事件回归分析与协变量调整估计出姑息治疗接受者的急诊科就诊率较高（HR=3.61，P<0.001），但粗略的PERR估计值和所提出的PERR估计值分别为1.45（P=0.22）和1.22（P=0.57）。结论：所提出的偏差减少方法减轻了PERR假设不存在事件依赖性被违反的影响。它允许更广泛地应用PERR方法。 显示英文摘要

摘要： Objectives: Prior event rate ratio (PERR) is a promising approach to control confounding in observational and real-world evidence research. One of its assumptions is that occurrence of outcome events does not influence later event rate, or in other words, absence of 'event dependence'. This study proposes, evaluates and illustrates a bias reduction method when this assumption is violated. Study Design and Setting: We propose the conditional frailty method for implementation of PERR in the presence of event dependence and evaluate its performance by simulation. We demonstrate the use of the method with a study of emergency department visit rate and palliative care in patients with advanced cancer in Singapore. Results: Simulations showed that, in the presence of negative (positive) event dependence, the crude PERR estimate of treatment effect was biased towards (away from) the null value. The proposed method successfully reduced the bias, with median of absolute level of relative bias at about 5%. Dynamic random-intercept modelling revealed positive event dependence in emergency department visits among patients with advanced cancer. While conventional time-to-event regression analysis with covariate adjustment estimated higher rate of emergency department visits among palliative care recipients (HR=3.61, P<0.001), crude PERR estimate and the proposed PERR estimate were 1.45 (P=0.22) and 1.22 (P=0.57), respectively. Conclusions: The proposed bias reduction method mitigates the impact of violation of the PERR assumption of absence of event dependence. It allows broader application of the PERR approach.