Rajeswaran Jeevanantham, Blackstone Eugene H
Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA.
Stat Methods Med Res. 2017 Feb;26(1):21-42. doi: 10.1177/0962280214537255. Epub 2016 Jul 11.
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.
在医学领域，我们经常会遇到本质上呈非线性的纵向时间关系。风险因素的影响在纵向随访过程中也可能发生变化。本文提出了一个多阶段非线性混合效应模型系统，用于对纵向连续测量的时间模式进行建模，并通过时间分解来识别每个阶段的阶段和风险因素。使用易于获得的统计软件，以肺移植后的肺活量测定数据为例说明了该模型的应用。该应用展示了我们的灵活模型在处理复杂非线性模式和时变系数时的实用性。
