How to Identify Subgroups in Longitudinal Clinical Data: Treatment Response Patterns in Patients With a Shortened Dental Arch
Year: 2022
Type: article
Abstract: BACKGROUND: When dental patients seek care, treatments are not always successful,that is patients' oral health problems are not always eliminated or substantially reduced. Identifying these patients (treatment non-responders) is essential for clinical decision-making. Group-based trajectory modeling (GBTM) is rarely used in dentistry, but a promising statistical technique to identify non-responders in particular and clinical distinct patient groups in general in longitudinal data sets. AIM: Using group-based trajectory modeling, this study aimed to demonstrate how to identify oral health-related quality of life (OHRQoL) treatment response patterns by the example of patients with a shortened dental arch (SDA). METHODS: This paper is a secondary data analysis of a randomized controlled clinical trial. In this trial SDA patients received partial removable dental prostheses replacing missing teeth up to the first molars (N = 79) either or the dental arch ended with the second premolar that was present or replaced by a cantilever fixed dental prosthesis (N = 71). Up to ten follow-up examinations (1-2, 6, 12, 24, 36, 48, 60, 96, 120, and 180 months post-treatment) continued for 15 years. The outcome OHRQoL was assessed with the 49-item Oral Health Impact Profile (OHIP). Exploratory GBTM was performed to identify treatment response patterns. RESULTS: Two response patterns could be identified - "responders" and "non-responders." Responders' OHRQoL improved substantially and stayed primarily stable over the 15 years. Non-responders' OHRQoL did not improve considerably over time or worsened. While the SDA treatments were not related to the 2 response patterns, higher levels of functional, pain-related, psychological impairment in particular, and severely impaired OHRQoL in general predicted a non-responding OHRQoL pattern after treatment. Supplementary, a 3 pattern approach has been evaluated. CONCLUSIONS: Clustering patients according to certain longitudinal characteristics after treatment is generally important, but specifically identifying treatment in non-responders is central. With the increasing availability of OHRQoL data in clinical research and regular patient care, GBTM has become a powerful tool to investigate which dental treatment works for which patients.
Authors: Oliver Schierz, Chi Hyun lee, Mike T. John, Angelika Rauch, Daniel R. Reissmann, Ralf Kohal, Birgit Marrè, Klaus Böning, Michael H. Walter, Ralph Gunnar Luthardt, Heike Rudolph, Torsten Mundt, Wolfgang Hannak, Guido Heydecke, Matthias Kern, Sinsa Hartmann, Julian Boldt, Helmut Stark, Daniel Edelhoff, Bernd Wöstmann, Stefan Wolfart, Florentine Jahn
Institutions: Leipzig University, University of Massachusetts Amherst, University of Minnesota, University Hospital Regensburg, University of Regensburg, University of Freiburg, University Hospital Carl Gustav Carus, Technische Universität Dresden, Universität Ulm, University Hospital Ulm, Universität Greifswald, Stiftung Charité, Charité - Universitätsmedizin Berlin, University Medical Center Hamburg-Eppendorf, Christian-Albrechts-Universität zu Kiel, Johannes Gutenberg University Mainz, University of Würzburg, University of Bonn, LMU Klinikum, Ludwig-Maximilians-Universität München, Justus-Liebig-Universität Gießen, RWTH Aachen University, Friedrich Schiller University Jena
Language: English
Subfield: Periodontics
Field: Dentistry
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,130
OpenAlex ID: W4306408139