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Cluster analysis for repeated data with dropout: Sensitivity analysis using a distal event.

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This study examines how missing data affects aortic diameter analysis. Results show that non-ignorable dropout models are crucial for accurately identifying patient subgroups and predicting aortic wall degeneration.

Keywords:
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Area of Science:

  • Cardiovascular research
  • Biostatistics
  • Medical imaging analysis

Background:

  • Aortic wall degeneration increases rupture risk, posing a life-threatening condition.
  • Cluster analysis of repeated aortic diameter measures identified patient subgroups in surveillance.
  • Previous analyses assumed missing data were random.

Purpose of the Study:

  • To assess the impact of different missing-data models on cluster analysis of aortic diameter.
  • To investigate the vulnerability of estimated trajectories and membership probabilities to non-ignorable dropout.

Main Methods:

  • Applied various missing-data models for non-ignorable dropout (Muthen et al., 2011).
  • Utilized cluster analysis on repeated measures of aortic diameter.
  • Evaluated the robustness of subgroup identification and trajectory estimation.

Main Results:

  • Non-ignorable dropout models significantly influenced cluster analysis outcomes.
  • Estimated trajectories and posterior membership probabilities varied based on the missing-data model used.
  • The assumption of missingness at random may lead to inaccurate subgroup identification.

Conclusions:

  • Robustness of cluster analysis for aortic diameter requires careful consideration of missing-data mechanisms.
  • Non-ignorable dropout models are essential for accurate patient stratification in aortic degeneration surveillance.
  • Findings highlight the need for advanced statistical methods in longitudinal cardiovascular studies.