Optimal method for including repeated measures in prediction model

What is the optimal method for including repeated measures of a biomarker in a prediction model…particularly if the time of measurement is not consistent between participants. I read this article but am still uncertain. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods | Diagnostic and Prognostic Research | Full Text (biomedcentral.com)

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If a formal approach is desired, I think state-space modeling is ideal. Note in particular that your problem (irregular timing of measurements) arises only from the customary statistician’s habit of treating the data as the ding an sich, rather than a mere phenomenon. The state-space approach, by contrast, posits an underlying state process (generally, multivariate) that evolves (generally) in continuous time, and gives rise to the data via a measurement process that is distinct from the underlying state process.