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Amna Klich1,2,3,4, René Ecochard1,2,3,4, Fabien Subtil1,2,3,4

  • 1Université de Lyon, Lyon, France.

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|April 7, 2021
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Summary
This summary is machine-generated.

Standard trajectory classification models in clinical research can be biased. New mixed models accounting for between-individual variance improve classification accuracy, especially with sufficient repeated measurements, leading to more clinically relevant patient groups.

Keywords:
ECM algorithmbetween-individual varianceclassificationlongitudinal datamixed modeltrajectories

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Area of Science:

  • Clinical research
  • Biostatistics
  • Longitudinal data analysis

Background:

  • Trajectory classification is crucial for understanding individual patient heterogeneity in clinical research.
  • Standard models often assume no between-individual variance within groups, potentially leading to biased classifications and overestimated error variance.
  • This limitation necessitates the development of more robust classification methods.

Purpose of the Study:

  • To develop and evaluate extensions of standard trajectory classification models using mixed models.
  • To assess the impact of incorporating between-individual variance on classification accuracy.
  • To compare the clinical relevance of classifications derived from standard versus extended models.

Main Methods:

  • Two extended mixed models were developed: one with equal between-individual variance across groups and another with unequal variance.
  • Simulations were conducted to evaluate the performance of the standard and extended models.
  • The models were applied to human chorionic gonadotropin trajectories after hydatidiform mole curettage.

Main Results:

  • The extended model with equal between-individual variance reduced misclassification by up to 50% compared to the standard model when true variance existed within groups.
  • The extended model with unequal variance further decreased misclassification by up to 11% when variance differed between groups.
  • Both extended models require a substantial number of repeated measurements for accurate adjustment.
  • Extended models yielded more clinically relevant classifications based on trajectory patterns, unlike the standard model which focused on levels.

Conclusions:

  • For studies with sufficient repeated measurements, employing a trajectory classification model that accounts for equal between-individual variance is recommended over standard models.
  • A model considering unequal between-individual variance may be beneficial in specific scenarios after initial assessment.
  • These advanced models offer improved accuracy and clinical relevance in trajectory analysis.