Probabilistic Multivariate Early Warning Signals

Laitinen, Ville; Lahti, Leo

doi:10.1007/978-3-031-15034-0_13

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…Furthermore, application to real-world data raises several new challenges not considered in theorical models ( Brett et al, 2018 ; Southall et al, 2021 ; Dablander et al, 2022 ), such as variation in temporal resolution ( Clements and Ozgul, 2018 ), as well as random ( Hillebrand et al, 2020 ) and extrinsic noise ( Qin and Tang, 2018 ). Although various EWSs have been proposed, with a growing interest for multivariate EWSs over the past years ( Eason et al, 2016 ; Chen S. et al, 2019 ; Laitinen and Lahti, 2022 ), few studies opted for a comparative approach (but see Dakos et al, 2012a ; Weinans et al, 2021 ). As the performance of EWSs might well be context-dependent ( Boettiger and Hastings, 2012 ; Weinans et al, 2021 ), comparative approaches to specific contexts/datasets could be very informative.…”

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confidence: 99%

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis

Legault,

Pu,

Weinans

et al. 2024

Front. Netw. Physiol.

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Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as “critical transitions,” by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

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