Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in
Leonard Roth1, Matthias Studer2, Emilie Zuercher3
1Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de La Corniche 10, 1010, Lausanne, Switzerland. leonard.roth@unisante.ch.
BMC Medical Research Methodology
|December 18, 2024
Summary
Standard sequence analysis may yield incorrect conclusions by ignoring sampling uncertainty. This study introduces a robust method to assess regression results, ensuring reliable findings in trajectory pattern analysis.
Area of Science:
- Social Sciences
- Biomedical Data Science
Background:
- Standard sequence analysis clusters trajectories but often ignores sampling uncertainty.
- This oversight can lead to inaccurate conclusions in regression models linking patterns to covariates.
Purpose of the Study:
- To introduce a novel procedure for assessing the robustness of regression results in sequence analysis.
- To account for sampling uncertainty in trajectory typology and associated regressions.
Main Methods:
- Utilized bootstrap samples to construct new typologies and estimate regression models.
- Employed a multilevel modeling framework, mimicking meta-analysis, to combine bootstrap estimates.
- Applied the methodology to healthcare utilization trajectories in a Swiss diabetic patient cohort.
Main Results:
- The procedure yields robust estimates and 95% prediction intervals, accounting for sampling uncertainty.
- Identified central and borderline trajectories within clusters.
- In the illustrative application, the association between lipid testing and healthcare utilization was not supported upon robustness assessment.
Conclusions:
- The developed Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) enhances the reliability of association studies.
- RARCAT is applicable to any analysis combining clustering with regression, extending beyond state sequence analysis.
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