Li Erning, Zhang Daowen, Davidian Marie
Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.
Comput Stat Data Anal. 2007 Aug 15;51(12):5776-5790. doi: 10.1016/j.csda.2006.10.008.
Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.
推断主要终点与连续反应纵向概况特征之间的关联是医学和公共卫生研究的核心关注点。通过主要数据和纵向数据对随机效应的共同依赖来表示这种关联的联合模型越来越受欢迎；然而，现有的推断方法可能效率低下或对随机效应分布的假设敏感。我们考虑一种半参数联合模型，该模型仅对这种分布做出适度假设，并开发基于似然的关联和分布推断，与现有方法相比，该方法具有更高的性能，且对真实随机效应分布不敏感。此外，估计的分布可以揭示有趣的总体特征，正如我们在一项关于围绝经期妇女纵向激素水平与骨骼状态之间关联的研究中所展示的那样。
