We propose a joint analysis of recurrent and nonrecurrent event data subject to general types of interval censoring. The proposed analysis allows for general semiparametric models, including the Box-Cox transformation and inverse Box-Cox transformation models for the recurrent and nonrecurrent events, respectively. A frailty variable is used to account for the potential dependence between the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty unspecified. We apply the pseudolikelihood for interval-censored recurrent event data, usually termed as panel count data, and the sufficient likelihood for interval-censored nonrecurrent event data by conditioning on the sufficient statistic for the frailty and using the working assumption of independence over examination times. Large sample theory and a computation procedure for the proposed analysis are established. We illustrate the proposed methodology by a joint analysis of the numbers of occurrences of basal cell carcinoma over time and time to the first recurrence of squamous cell carcinoma based on a skin cancer dataset, as well as a joint analysis of the numbers of adverse events and time to premature withdrawal from study medication based on a scleroderma lung disease dataset.

中文翻译：

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使用无分布脆弱性分析对面板计数和区间删失数据进行联合分析

我们建议对受一般类型的区间删失约束的复发性和非复发性事件数据进行联合分析。建议的分析允许使用一般的半参数模型，包括分别用于复发和非复发事件的 Box-Cox 变换和逆 Box-Cox 变换模型。虚弱变量用于解释复发性和非复发性事件过程之间的潜在依赖性，同时未指定虚弱的分布。我们将伪似然应用于间隔删失的复发事件数据，通常称为面板计数数据，并通过对虚弱的足够统计数据进行调节并使用独立于检查时间的工作假设来应用间隔删失的非复发事件数据的充分似然。建立了大样本理论和建议分析的计算程序。我们通过基于皮肤癌数据集对基底细胞癌随时间和鳞状细胞癌首次复发的发生次数进行联合分析，以及对不良事件和根据硬皮病肺病数据集提前退出研究药物的时间。