Buzkova Petra
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Int J Biostat. 2010;6(1):Article 30. doi: 10.2202/1557-4679.1239.
When patients are monitored for potentially recurrent events such as infections or tumor metastases, it is common for clinicians to ask patients to come back sooner for follow-ups based on the results of the most recent exam. This means that subjects' observation times will be irregular and related to subject-specific factors. Previously proposed methods for handling such panel count data assume that the dependence between the events process and the observation time process is governed by time-independent factors. This article considers situations where the observation times are predicted by time-varying factors such as the outcome observed at the last visit or cumulative exposure. Using a joint modelling approach, we propose a class of inverse-intensity-rate-ratio weighted estimators that are root-n consistent and asymptotically normal. The proposed estimators use estimating equations and are fairly simple and easy to compute. We demonstrate the performance of the method using simulated data and illustrate the approach using a cancer study dataset.
当对患者进行潜在复发事件（如感染或肿瘤转移）监测时，临床医生通常会根据最近一次检查的结果要求患者提前回来进行随访。这意味着受试者的观察时间将是不规则的，并且与受试者的特定因素相关。先前提出的处理此类面板计数数据的方法假定事件过程与观察时间过程之间的依赖性由与时间无关的因素控制。本文考虑观察时间由时变因素预测的情况，例如上次就诊时观察到的结果或累积暴露量。使用联合建模方法，我们提出了一类逆强度率比加权估计量，它们是根n一致且渐近正态的。所提出的估计量使用估计方程，相当简单且易于计算。我们使用模拟数据展示了该方法的性能，并使用癌症研究数据集说明了该方法。
