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Extended Joinpoint Regression Methodology for Complex Survey Data.

Benmei Liu1, Hyune-Ju Kim2, Joe Zou3

  • 1Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.

Statistics in Medicine
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

New statistical models for analyzing trends in health survey data improve accuracy by using individual-level data. This approach correctly handles complex sample designs and correlations between time points, leading to more reliable joinpoint regression analyses.

Keywords:
complex survey dataindividual‐level modeljoinpoint regressionmodified design‐based AICtrend analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Joinpoint regression models trends in aggregated time-specific estimates, primarily for non-survey data.
  • Existing methods struggle with complex survey data, which has correlations between time-specific estimates and incorrect degrees of freedom calculations.

Purpose of the Study:

  • To develop and evaluate individual-level joinpoint regression models for complex survey data.
  • To address issues of inter-time point correlation and correct degrees of freedom in survey data analysis.
  • To propose a modified design-based Akaike Information Criterion (M-dAIC) for model selection in complex survey designs.

Main Methods:

  • Proposed individual-level models incorporating correlation between time points and correcting degrees of freedom for sampling designs.
  • Introduced a modified design-based Akaike Information Criterion (M-dAIC) for model selection.
  • Empirically compared new methods with existing aggregate-level models using simulation studies and health survey data.

Main Results:

  • Individual-level models accurately identified the true number of joinpoints in complex survey data.
  • The proposed methods demonstrated superior performance compared to established aggregate-level models, especially with moderate to large interclass correlation coefficients (ICC).

Conclusions:

  • Individual-level joinpoint regression models offer a more accurate approach for analyzing trends in complex survey data.
  • The developed methods and M-dAIC provide robust tools for statistical inference and model selection in health survey research.