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Evaluation of Methods for Joinpoint Analysis of Time Series Using Simulated and Real-World Data.

Lucie Noé1, Zaba Valtuille1, Emilie Lanoy2

  • 1Center of Clinical Investigations, INSERM CIC1426, Robert Debré University Hospital, APHP.Nord, Paris, France; Université Paris Cité, UMR 1343, Perinatal and Pediatric Pharmacology and Therapeutic Assessment, Paris, France.

Journal of Clinical Epidemiology
|September 3, 2025
PubMed
Summary

This study compared Joinpoint Regression Program (JRP) and R 'segmented' package for trend analysis. R showed higher specificity without autocorrelation, while JRP performed better with autocorrelated healthcare data.

Keywords:
Joinpoint analysishealth carejoinpoint regressionmental healthreal-world datasimulation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Joinpoint regression (JR) is crucial for identifying trend changes in time series data.
  • Selecting appropriate software for JR is vital for accurate analysis of healthcare trends.

Purpose of the Study:

  • To compare the performance of the Joinpoint Regression Program (JRP) and the R 'segmented' package in detecting joinpoints.
  • To evaluate software performance using simulated data with varying characteristics and real-world pediatric mental health hospitalization data.

Main Methods:

  • Simulated 1000 datasets with controlled autocorrelation, trend changes, and joinpoint locations.
  • Analyzed monthly pediatric mental health hospitalization proportions (2016-2023).
  • Evaluated accuracy, specificity, confidence interval coverage, and monthly percent change (MPC) coverage.

Main Results:

  • In simulations without autocorrelation, R showed higher specificity (97.9%) than JRP (92.7%).
  • With trend changes and autocorrelation, JRP demonstrated better accuracy and confidence interval coverage compared to R.
  • Analysis of pediatric mental health hospitalizations revealed differences in detected joinpoints and average monthly percent change between JRP and R.

Conclusions:

  • The R 'segmented' package is suitable for datasets lacking residual autocorrelation.
  • JRP is recommended for analyzing autocorrelated healthcare data or data with no significant trend changes.
  • Software choice for joinpoint regression should align with specific dataset characteristics for reliable trend analysis.