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Predictive Partly Conditional Models for Longitudinal Ordinal Outcomes with Application to Alzheimer’s Disease Progression

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  • Jacquelyn E. Neal

    (Vanderbilt University)

  • Panpan Zhang

    (Vanderbilt University)

  • Dandan Liu

    (Vanderbilt University)

Abstract

Assessing time-dependent risk factors in relation to the risk of disease progression is challenging, yet important, especially for chronic diseases with slow progression. In this paper, the partly conditional model is extended for characterizing disease progression at time t with longitudinal ordinal outcomes in the presence of time-dependent covariates at time s $$(s

Suggested Citation

  • Jacquelyn E. Neal & Panpan Zhang & Dandan Liu, 2025. "Predictive Partly Conditional Models for Longitudinal Ordinal Outcomes with Application to Alzheimer’s Disease Progression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(2), pages 233-250, July.
    • Handle: RePEc:spr:stabio:v:17:y:2025:i:2:d:10.1007_s12561-024-09433-w
    DOI: 10.1007/s12561-024-09433-w
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    References listed on IDEAS

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