用于主要终点和纵向过程的联合模型中的潜在模型稳健性。
Latent-model robustness in joint models for a primary endpoint and a longitudinal process.
作者信息
Huang Xianzheng, Stefanski Leonard A, Davidian Marie
机构信息
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA.
出版信息
Biometrics. 2009 Sep;65(3):719-27. doi: 10.1111/j.1541-0420.2008.01171.x. Epub 2009 Jan 23.
Abstract
Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.
摘要
通过共享随机效应将主要反应和纵向过程进行联合建模在许多应用领域中被广泛使用。基于似然的联合模型推断需要对随机效应进行模型设定。随机效应的不恰当模型设定可能会损害推断。我们提出了一些方法来诊断导致联合模型推断有偏差的那种随机效应模型误设情况。通过应用于模拟数据，以及应用于一项围绝经期妇女骨密度研究的数据和一项HIV临床试验的数据来说明这些方法。
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