PreprintCC BY 4.0arXiv
Should the pharmaceutical industry consider using high-order autoregressive models when modelling panel data?
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.
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BibTeX
@misc{jackson2026should,
title = {{Should the pharmaceutical industry consider using high-order autoregressive models when modelling panel data?}},
author = {Dan Jackson and Fanni Zhang and Abdul-Azeez Ganiyu and Rose Baker},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.26405},
eprint = {2609.26405},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.26405},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Should the pharmaceutical industry consider using high-order autoregressive models when modelling panel data?
AU - Dan Jackson
AU - Fanni Zhang
AU - Abdul-Azeez Ganiyu
AU - Rose Baker
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.26405
UR - https://arxiv.org/abs/2609.26405
AB - 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.
ER -