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The temporal event-based model: learning event timelines in progressive diseases
Journal article   Open access

The temporal event-based model: learning event timelines in progressive diseases

Peter A. Wijeratne, Arman Eshaghi, William J. Scotton, Maitrei Kohli, Leon Aksman, Neil P. Oxtoby, Dorian Pustina, John H. Warner, Jane S. Paulsen, Rachael I. Scahill, …
Imaging Neuroscience, Vol.1, pp.1-19
08/10/2023
DOI: 10.1162/imag_a_00010
PMCID: PMC10503481
PMID: 37719837
url
https://doi.org/10.1162/imag_a_00010View
Published (Version of record) Open Access

Abstract

Abstract Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer’s disease (AD) and Huntington’s disease (HD). In both diseases, the TEBM recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

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