Longitudinal data frequently arise in many fields such as medical follow-up studies focusing on specific longitudinal responses. In such situations, the responses are recorded only at discrete observation times. Most existing approaches for longitudinal data analysis assume that the observation or follow-up times are independent of the underlying response process, either completely or given some known covariates. We present a joint analysis approach in which possible correlations among the responses, observation and follow-up times can be characterized by time-dependent random effects. Estimating equations are developed for parameter estimation and the resulting estimates are shown to be consistent and asymptotically normal. A simulation study is conducted to assess the finite sample performance of the approach and the method is applied to data arising from a skin cancer study.

中文翻译：

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具有信息观测时间的纵向数据的半参数随机效应模型


纵向数据经常出现在许多领域，例如关注特定纵向反应的医学随访研究。在这种情况下，仅在离散的观察时间记录响应。大多数现有的纵向数据分析方法都假设观察或后续时间独立于潜在的响应过程，或者完全独立，或者在给定一些已知的协变量的情况下。我们提出了一种联合分析方法，其中响应、观察和随访时间之间可能的相关性可以通过时间相关的随机效应来表征。开发了用于参数估计的估计方程，并且结果估计结果是一致的且渐近正态的。进行模拟研究以评估该方法的有限样本性能，并将该方法应用于皮肤癌研究产生的数据。