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Using sequence and cluster analysis to characterize variables that unfold over time: implementation and practical

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Sequence and cluster analysis can characterize longitudinal health trajectories. This guide helps epidemiologists apply these methods to understand event timing, order, and duration for better health outcome insights.

Keywords:
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Area of Science:

  • Epidemiology
  • Biostatistics
  • Social Sciences

Background:

  • Longitudinal trajectories of social, health, or environmental variables present a persistent characterization challenge.
  • Sequence and cluster analysis offer data-driven approaches to differentiate event timing, order, and duration.

Purpose of the Study:

  • Provide practical guidance for epidemiologists on implementing sequence and cluster analysis.
  • Offer clear advice on decision points and tradeoffs in applying these methods.
  • Facilitate understanding of longitudinal data analysis for health research.

Main Methods:

  • Introduce three core steps: coding trajectories, measuring sequence dissimilarity, and grouping similar trajectories.
  • Discuss decision points including data cleaning, dissimilarity measures, and clustering algorithms.
  • Apply sequence analysis to transition-to-retirement trajectories (ages 51-75) using Health and Retirement Study data.

Main Results:

  • Demonstrate the creation and grouping of diverse transition-to-retirement trajectories.
  • Estimate associations between identified retirement trajectory groups and self-rated health.
  • Highlight the utility of sequence analysis in understanding complex life course events.

Conclusions:

  • Sequence and cluster analysis are valuable tools for epidemiologists analyzing longitudinal data.
  • This guide aids researchers in navigating the analytic decisions and implementation challenges of sequence analysis.
  • The approach offers deeper insights into the relationship between life course trajectories and health outcomes.