Abstract
Background
Although patients have different treatment preferences, these individual preferences could often be grouped in subgroups with shared preferences. Knowledge of these subgroups as well as factors associated with subgroup membership supports health care professionals in the understanding of what matters to patients in clinical decision-making.
Objectives
To identify subgroups of patients with rheumatoid arthritis (RA) based on their shared preferences toward disease-modifying antirheumatic drugs (DMARDs), and to identify factors associated with subgroup membership.
Methods
A discrete choice experiment to determine DMARD preferences of adult patients with RA was designed based on a literature review, expert recommendations, and focus groups. In this multicenter study, patients were asked to state their preferred choice between two different hypothetical treatment options, described by seven DMARD characteristics with three levels within each characteristic. Latent class analyses and multinomial logistic regressions were used to identify subgroups and the characteristics (patient characteristics, disease-related variables, and beliefs about medicines) associated with subgroup membership.
Results
Among 325 participating patients with RA, three subgroups were identified: an administration-driven subgroup (45.6%), a benefit-driven subgroup (29.7%), and a balanced subgroup (24.7%). Patients who were currently using biologic DMARDs were significantly more likely to belong to the balanced subgroup than the administration-driven subgroup (relative risk ratio (RRR): 0.50, 95% CI: 0.28–0.89). Highly educated patients were significantly more likely to belong to the benefit-driven subgroup than the balanced subgroup (RRR: 11.4, 95% CI: 0.97–133.6). Patients’ medication-related concerns did not contribute significantly to subgroup membership, whereas a near-significant association was found between patients’ beliefs about medication necessity and their membership of the benefit-driven subgroup (RRR: 1.12, 95% CI: 1.00–1.23).
Conclusion
Three subgroups with shared preferences were identified. Only biologic DMARD use and educational level were associated with subgroup membership. Integrating patient’s medication preferences in pharmacotherapy decisions may improve the quality of decisions and possibly medication adherence.
Acknowledgments
The authors would like to thank all patients who participated in this study, as well as the patient research partners involved in the design phase and pilot test of the DCE. Special thanks are extended to C. Delnoy-Meijers (MUMC+), J. Geusen (MUMC+), M. Hegeman (MST), and M. Hermus (Erasmus MC) for their assistance in patient recruitment and data collection on site. This research was supported financially by a grant from Pfizer. The abstract of this manuscript was presented at the EULAR 2018 (13–16 June, Amsterdam) and at the 47th European Symposium on Clinical Pharmacy (ESCP 2018, 24–26 October, Belfast). This abstract was published in the Annals of the Rheumatic Diseases 2018;77:188–189.
Disclosure
Dr van Heuckelum reports grants from Pfizer, during the conduct of the study. Dr Vervloet reports grants from Pfizer, during the conduct of the study; grants from Pfizer, grants from AbbVie and grants from AstraZenica, outside the submitted work. Prof. Dr Boonen reports grants from Abbvie, grants from Celgene, personal fees from UCB Pharma, personal fees from Lilly, personal fees from Sandoz, personal fees from Novartis and personal fees from Janssen Pharma, outside the submitted work. Prof. Dr van Dijk reports grants from Pfizer, during the conduct of the study; grants from Pfizer, grants from Abbvie and grants from Astra Zeneca, outside the submitted work. The authors report no other conflicts of interest in this work.
Supplementary material
Table S1 Example of a random choice task. Twelve random choice tasks without an “opt-out” or “no-treatment” option were included in the discrete choice experiment.
Table S2 Example of a dominant fixed choice task. Two dominant fixed choice tasks without an “opt-out” or “no-treatment” option were included in the discrete choice experiment.
Table S3 Final settings latent class analysis (Lighthouse Studio: CBC, Sawtooth Software)
Table S4 Results of the identification process of latent classes in this study
Table S5 Adjusted multinomial logistic regression model to identify factors associated with subgroup membership. Reference categories for categorical patient variables were: employment status (unpaid), education level (low), current bDMARD use (no), and educational level × complexity of the online survey (low). Educational level was classified in low, moderate and high educational level. Low educational level was defined as no education, (extended) primary education or pre-vocational education, whereas high educational level was defined as education provided by universities of applied sciences and research universities