We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

中文翻译：

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稀疏异步纵向数据的回归分析。

我们考虑针对稀疏异步纵向观测的回归模型的估计，在这些模型中，对象间断地观察到时间相关的响应和协变量。与同步数据不同，同步数据在同一时间点观察到响应和协变量，而异步数据则观察时间不匹配。针对协变量过程的光滑度假设，提出了与时间常数或时间相关系数有关的广义线性模型的简单核加权估计方程，该方程类似于同步数据。对于具有时不变系数或时变系数的模型，估计量是一致且渐近正态的，但收敛速度比同步数据慢。仿真研究表明，该方法在实际样本量下效果良好，并且可能优于基于临时最后值结转方法的用于同步数据的方法的简单应用。该方法的实用性在人类免疫缺陷病毒研究的数据中得到了说明。