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Longitudinal individual predictions from irregular repeated measurements data
Iris Eekhout1, Stef van Buuren2,3, Bram Visser4
1The Netherlands Organization for Applied Scientific Research (TNO), Child Health, Leiden, The Netherlands. iris.eekhout@tno.nl.
Scientific Reports
|January 18, 2023
Summary
This study presents a three-step method for accurate prediction using irregular intensive longitudinal data. The approach effectively models nonlinear relationships and intermittent measurements, demonstrated in piglet weight prediction.
Area of Science:
- Biostatistics
- Data Science
- Animal Science
Background:
- Intensive longitudinal data analysis is crucial for understanding input-outcome relationships.
- Nonlinearities and irregular measurement times complicate accurate modeling.
Purpose of the Study:
- Develop and evaluate a prediction model for irregular intensive longitudinal data.
- Create a tool for daily monitoring and prediction applicable to various fields.
Main Methods:
- A three-step process involving normalizing transformations for nonlinearities.
- Utilizing a broken-stick model to align intermittent time points.
- Selecting and evaluating covariates for accurate prediction.
Main Results:
- The developed model accurately predicts future outcomes.
- It accommodates nonlinear input-output relationships and individual measurement histories.
- Successfully applied to piglet weight prediction.
Conclusions:
- The methodology provides an optimal way to handle intensive irregular longitudinal data.
- The developed tool is effective for piglet weight prediction and adaptable to other applications.
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