Miglioretti Diana L
Group Health Cooperative, Center for Health Studies, 1730 Minor Ave. Suite 1600, Seattle, Washington 98101, USA.
Biometrics. 2003 Sep;59(3):710-20. doi: 10.1111/1541-0420.00082.
Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain.
健康状况是一个复杂的结果，通常由多种指标来表征。在评估健康状况随时间的变化时，通常会纵向收集多种指标。当结果是连续数据、分类数据和计数数据的组合时，这些多变量纵向数据带来的分析挑战会更加复杂。为应对这些挑战，我们提出一种完全贝叶斯潜在转变回归方法，用于联合分析来自任何分布的纵向结果的混合数据。健康状况被假定为一个分类潜在变量，多个结果被视为潜在健康状态的替代指标，存在测量误差。使用这种方法，基线潜在健康状态患病率以及随时间在健康状态之间转变的概率都被建模为协变量的函数。观察到的结果通过回归模型与潜在健康状态相关，这些回归模型包括个体特定效应以解释随时间重复测量之间的残差相关性，以及协变量效应以解释潜在健康状态的差异测量。我们用一项背痛纵向研究的数据来说明我们的方法。
