Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis

Article Article scientifique Article Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis eng 2024-12-18 author Roth Léonard Department 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. 765633 author Studer Matthias 0000-0002-6269-1412 Centre LIVES, University of Geneva, Boulevard du Pont-d'Arve 40, 1205, Geneva, Switzerland. 511630 Service enseignement, recherche, administratif et technique (SdS) (SCSPS) author Zuercher Emilie Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de La Corniche 10, 1010, Lausanne, Switzerland. author Peytremann-Bridevaux Isabelle Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de La Corniche 10, 1010, Lausanne, Switzerland. 10.1186/s12874-024-02435-8 39695379 1471-2288 PMC11654397 https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02435-8 Background: In standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest. Methods: We introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients. Results: The procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment. Conclusions: Investigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis. Association study Bootstrap Cluster analysis Multilevel modelling Regression analysis Sampling uncertainty Sampling variation Sequence Analysis Statistical inference Typology of trajectories Dewey 304.6/305.3/306 University of Lausanne Swiss National Science Foundation Strengthening Sequence Analysis 204740 CH Projects 195 Institut de démographie et de socioéconomie TH false 1282 Centre LIVES BMC Medical research methodology 24 1 2f72f662-eacb-44d1-abee-f7c20915380d Article (Published version) Public Article_scientifique_from_crossref.pdf 2372151 application/pdf CC BY-4.0 6fdca5407bbc053b82f98dfd9933265a96d67fdf5d5183c6b8b18d2aaba94381 openAccess Faculté des sciences de la société / Institut de démographie et de socioéconomie

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Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis	BMC Medical research methodology			2024	19	5

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