Recurrent events are often encountered in medical follow up studies. In addition, such recurrences have other quantities associated with them that are of considerable interest, for instance medical costs of the repeated hospitalizations and tumor size in cancer recurrences. These processes can be viewed as point processes, i.e. processes with arbitrary positive jump at each recurrence. An analysis of the mean function for such point processes have been proposed in the literature. However, such point processes are often skewed, leading to median as a more appropriate measure than the mean. Furthermore, the analysis of recurrent event data is often complicated by the presence of death. We propose a semiparametric model for assessing the effect of covariates on the quantiles of the point processes. We investigate both the finite sample as well as the large sample properties of the proposed estimators. We conclude with a real data analysis of the medical cost associated with the treatment of ovarian cancer.

中文翻译：

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在终端事件存在下与重复事件相关的重复测量的中位数分析。

在医学随访研究中经常遇到复发事件。另外，这种复发还具有其他与之相关的其他量，例如，重复住院的医疗费用和癌症复发中的肿瘤大小。这些过程可以看作是点过程，即每次重复出现任意正跳的过程。在文献中已经提出了针对这种点过程的均值函数的分析。但是，这样的点过程经常会出现偏差，导致中位数比平均值更合适。此外，经常性事件数据的分析通常因死亡而变得复杂。我们提出了一个半参数模型，用于评估协变量对点过程分位数的影响。我们调查了有限样本以及拟议估计量的大样本性质。我们以与卵巢癌治疗相关的医疗费用的真实数据分析作为结束。