AI 中文总结
本文主张在制药行业面板数据建模中，当非结构化或Toeplitz相关结构不可行时，采用高阶自回归模型作为灵活框架，以有效处理相关性。
AI 中文摘要
纵向数据通常以面板数据的形式出现，在制药行业中十分常见。然而，在使用重复测量混合模型（MMRM）时，并不总是能够拟合非结构化协方差矩阵。在此，我们认为当更一般的相关结构（如非结构化和/或Toeplitz结构）不可行时，高阶自回归模型提供了一种有用的框架。一阶自回归模型常被推荐使用，但根据我们的经验，更高阶的模型很少被考虑。我们提出了使用自回归模型（可能具有高阶）作为灵活框架来建模面板数据中相关性的理由，尤其是在更一般的结构难以识别的情况下。
英文摘要
Longitudinal data, often in the form of panel data, are commonly encountered in the pharmaceutical industry. However it is not always possible to fit an unstructured covariance matrix when using the Mixed Model for Repeated Measures (MMRM). Here we argue that high-order autoregressive models provide a useful framework when more general correlation structures, such as the unstructured and/or Toeplitz, are unfeasible. The autoregressive model of order one is often suggested but, in our experience, higher orders are seldom considered. We present the case for using autoregressive models, with potentially high orders, as a flexible framework to model correlations in panel data, when more general structures are hard to identify.
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