我们考虑多类型事件的纵向数据分析，其中某些事件在随访期间的某些时间点上以更粗略的水平（例如分组）被观察到，例如，某些事件（如疾病进展）只在某些受试者的随访的部分时间内可观察到，导致数据中存在间隙，或者当死亡时间可观察但死因未知时。在这种情况下，事件历史记录的关键特征（如发病时间、状态持续时间和事件数量）存在缺失数据。我们在独立和非信息粗化下推导出似然函数、得分和观察信息，并进行模拟研究，比较基于直接最大似然、蒙特卡罗期望最大化、忽略粗化从而表现为未发生事件以及在第一次粗化时进行人造右截尾的估计器的偏差、经验标准误差和置信区间覆盖率。我们使用关于前列腺癌姑息治疗的男性药物处方和生存的纵向数据来估计生成模型的参数之一。我们证明了性能取决于多个因素，包括样本大小和粗化类型。

We consider the analysis of longitudinal data of multiple types of events where some of the events are observed on a coarser level (e.g. grouped) at some time points during the follow-up, for example, when certain events, such as disease progression, are only observable during parts of follow-up for some subjects, causing gaps in the data, or when the time of death is observed but the cause of death is unknown. In this case, there is missing data in key characteristics of the event history such as onset, time in state, and number of events. We derive the likelihood function, score and observed information under independent and non-informative coarsening, and conduct a simulation study where we compare bias, empirical standard errors, and confidence interval coverage of estimators based on direct maximum likelihood, Monte Carlo Expectation Maximisation, ignoring the coarsening thus acting as if no event occurred, and artificial right censoring at the first time of coarsening. Longitudinal data on drug prescriptions and survival in men receiving palliative treatment for prostate cancer is used to estimate the parameters of one of the data-generating models. We demonstrate that the performance depends on several factors, including sample size and type of coarsening.