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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.19127 |
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| _version_ | 1866912292652711936 |
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| author | Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L. B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P Kosorok, Michael R. |
| author_facet | Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L. B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P Kosorok, Michael R. |
| contents | Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest average treatment effects$\unicode{x2013}$potentially due to individual variation in treatment response and the diverse etiologies of cLBP. This multi-site sequential, multiple-assignment randomized trial (SMART) investigated four treatment modalities with two stages of randomization and aimed to enroll 630 protocol completers. The primary objective was to develop a precision medicine approach by estimating optimal treatment or treatment combinations based on patient characteristics and initial treatment response. The analysis strategy focuses on estimating interpretable dynamic treatment regimes and identifying subgroups most responsive to specific interventions. Broad eligibility criteria were implemented to enhance generalizability and recruitment, most notably that participants could be eligible to enroll even if they could not be assigned to one (but no more) of the study interventions. Enrolling participants with restrictions on the treatment they could be assigned necessitated modifications to standard minimization methods for balancing covariates. The BEST trial represents one of the largest SMARTs focused on clinical decision-making to date and the largest in cLBP. By collecting an extensive array of biomarker and phenotypic measures, this trial may identify potential treatment mechanisms and establish a more evidence-based approach to individualizing cLBP treatment in clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Design and Rationale of the Biomarkers for Evaluating Spine Treatments (BEST) Trial Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L. B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P Kosorok, Michael R. Applications Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest average treatment effects$\unicode{x2013}$potentially due to individual variation in treatment response and the diverse etiologies of cLBP. This multi-site sequential, multiple-assignment randomized trial (SMART) investigated four treatment modalities with two stages of randomization and aimed to enroll 630 protocol completers. The primary objective was to develop a precision medicine approach by estimating optimal treatment or treatment combinations based on patient characteristics and initial treatment response. The analysis strategy focuses on estimating interpretable dynamic treatment regimes and identifying subgroups most responsive to specific interventions. Broad eligibility criteria were implemented to enhance generalizability and recruitment, most notably that participants could be eligible to enroll even if they could not be assigned to one (but no more) of the study interventions. Enrolling participants with restrictions on the treatment they could be assigned necessitated modifications to standard minimization methods for balancing covariates. The BEST trial represents one of the largest SMARTs focused on clinical decision-making to date and the largest in cLBP. By collecting an extensive array of biomarker and phenotypic measures, this trial may identify potential treatment mechanisms and establish a more evidence-based approach to individualizing cLBP treatment in clinical practice. |
| title | Statistical Design and Rationale of the Biomarkers for Evaluating Spine Treatments (BEST) Trial |
| topic | Applications |
| url | https://arxiv.org/abs/2503.19127 |