A great deal of literature has been established for regression analysis of longitudinal data but most of the existing methods assume that covariates can be observed completely or at the same observation times for the response variable, and the observation process is independent of the response variable completely or given covariates. As pointed out by many authors, in practice, one may face the situation where the response variable and covariates are observed intermittently at different time points, leading to sparse asynchronous longitudinal data, or the observation process may be related to the response variable even given covariates. It is apparent that sometimes both issues can occur in the same time and although some literature has been developed to address each of the two issues, it does not seem to exist an established approach that can deal with both together. To address this, in this paper, a flexible semiparametric transformation conditional model is presented and for estimation, a kernel-weighted estimating equation-based approach is proposed. The proposed estimators of regression parameters are shown to be consistent and asymptotically follow the normal distribution. For the assessment of the finite sample performance of the method, an extensive simulation study is carried out and suggests that it performs well for practical situations. The approach is applied to a prospective HIV study that motivated this investigation.

已经建立了大量的文献用于纵向数据的回归分析，但是大多数现有方法都假设可以完全观察协变量或在相同观察时间观察响应变量，并且观察过程完全独立于响应变量或给定协变量。正如许多作者所指出的，在实践中，可能会遇到这样一种情况，即在不同的时间点间断地观察响应变量和协变量，导致稀疏的异步纵向数据，或者即使给定了协变量，观察过程也可能与响应变量有关。 。显然，有时两个问题可能同时发生，尽管已经开发了一些文献来解决这两个问题，它似乎不存在可以同时解决这两个问题的既定方法。为了解决这个问题，本文提出了一种灵活的半参数转换条件模型，并提出了一种基于核加权估计方程的估计方法。回归参数的估计估计值是一致的，并且渐近遵循正态分布。为了评估该方法的有限样本性能，进行了广泛的模拟研究，结果表明该方法在实际情况下表现良好。该方法适用于前瞻性HIV研究，该研究激发了这项研究。提出了一种基于核加权估计方程的方法。回归参数的估计估计值是一致的，并且渐近遵循正态分布。为了评估该方法的有限样本性能，进行了广泛的模拟研究，结果表明该方法在实际情况下表现良好。该方法适用于前瞻性HIV研究，该研究激发了这项研究。提出了一种基于核加权估计方程的方法。回归参数的估计估计值是一致的，并且渐近遵循正态分布。为了评估该方法的有限样本性能，进行了广泛的仿真研究，结果表明该方法在实际情况下表现良好。该方法适用于前瞻性HIV研究，该研究激发了这项研究。