Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence

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dc.contributor.authorDiop, Awa
dc.contributor.authorGupta, Alind
dc.contributor.authorMueller, Sabrina
dc.contributor.authorDron, Louis
dc.contributor.authorHarari, Ofir
dc.contributor.authorBerringer, Heather
dc.contributor.authorKalatharan, Vinusha
dc.contributor.authorPark, Jay J.H.
dc.contributor.authorMESIDOR, Miceline
dc.contributor.authorTalbot, Denis
dc.date.accessioned2024-03-15T21:02:54Z
dc.date.available2024-03-15T21:02:54Z
dc.date.issued2024
dc.description.abstractIt is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationDiop, A., Gupta, A., Mueller, S., Dron, L., Harari, O., Berringer, H., Kalatharan, V., Park, J. J., Mésidor, M., & Talbot, D. (2024). Assessing the performance of groupbased trajectory modeling method to discover different patterns of medication adherence. Pharmaceutical Statistics. https://doi.org/10.1002/pst.2365
dc.identifier.urihttps://doi.org/10.1002/pst.2365
dc.identifier.urihttps://hdl.handle.net/1828/16109
dc.language.isoen
dc.publisherPharmaceutical Statistics
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleAssessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence
dc.typeArticle

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