St. Jude Children's Research Hospital, Department of Biostatistics, 262 Danny Thomas Place, Memphis, TN 38105, USA.
Stat Med. 2011 May 30;30(12):1429-40. doi: 10.1002/sim.4198. Epub 2011 Feb 22.
Longitudinal data analysis is one of the most discussed and applied areas in statistics and a great deal of literature has been developed for it. However, most of the existing literature focus on the situation where observation times are fixed or can be treated as fixed constants. This paper considers the situation where these observation times may be random variables and more importantly, they may be related to the underlying longitudinal variable or process of interest. Furthermore, covariate effects may be time-varying. For the analysis, a joint modeling approach is proposed and in particular, for estimation of time-varying regression parameters, an estimating equation-based procedure is developed. Both asymptotic and finite sample properties of the proposed estimates are established. The methodology is applied to an acute myeloid leukemia trial that motivated this study.
纵向数据分析是统计学中讨论和应用最广泛的领域之一，为此已经产生了大量的文献。然而，现有的大多数文献都集中在观察时间固定或可以视为固定常数的情况下。本文考虑了这些观察时间可能是随机变量的情况，更重要的是，它们可能与潜在的纵向变量或感兴趣的过程有关。此外，协变量的影响可能随时间变化。为此，提出了一种联合建模方法，特别是对于时变回归参数的估计，开发了一种基于估计方程的方法。建立了所提出估计的渐近和有限样本性质。该方法应用于激发这项研究的急性髓细胞性白血病试验。
