Problem
Research questions and friction points this paper is trying to address.
pharmaceutical industry
high-order autoregressive models
panel data
unstructured covariance matrix
mixed model for repeated measures
Should the pharmaceutical industry consider using high-order autoregressive models when modelling panel data?
🤖 AI Summary
论文探讨了在难以使用非结构化协方差矩阵时，采用高阶自回归模型作为处理制药行业面板数据相关性的一种有效方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
high-order autoregressive models
panel data
unstructured covariance matrix
mixed model for repeated measures
correlation structures
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