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Hua LH, Bar-Or A, Cohan SL, Lublin FD, Coyle PK, Cree BA, Meng X, Su W, Cox GM, Fox RJ. Effects of baseline age and disease duration on the efficacy and safety of siponimod in patients with active SPMS: Post hoc analyses from the EXPAND study. Mult Scler Relat Disord 2023; 75:104766. [PMID: 37245350 DOI: 10.1016/j.msard.2023.104766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND Older age and longer disease duration (DD) may impact the effectiveness of disease-modifying therapies in patients with multiple sclerosis (MS). Siponimod is a sphingosine 1-phosphate receptor modulator approved for the treatment of active secondary progressive MS (SPMS) in many countries. The pivotal phase 3 EXPAND study examined siponimod versus placebo in a broad SPMS population with both active and non-active disease. In this population, siponimod demonstrated significant efficacy, including a reduction in the risk of 3-month confirmed disability progression (3mCDP) and 6-month confirmed disability progression (6mCDP). Benefits of siponimod were also observed across age and DD subgroups in the overall EXPAND population. Herein we sought to assess the clinical impact of siponimod across age and disease duration subgroups, specifically in participants with active SPMS. METHODS This study is a post hoc analysis of a subgroup of EXPAND participants with active SPMS (≥ 1 relapse in the 2 years before the study and/or ≥ 1 T1 gadolinium-enhancing magnetic resonance imaging lesion at baseline) receiving oral siponimod (2 mg/day) or placebo during EXPAND. Data were analyzed for participant subgroups stratified by age at baseline (primary cut-off: < 45 year ≥ 45 years; and secondary cut-off: < 50 years or ≥ 50 years) and by DD at baseline (< 16 years or ≥ 16 years). Efficacy endpoints were 3mCDP and 6mCDP. Safety assessments included adverse events (AEs), serious AEs, and AEs leading to treatment discontinuation. RESULTS Data from 779 participants with active SPMS were analyzed. All age and DD subgroups had 31-38% (3mCDP) and 27-43% (6mCDP) risk reductions with siponimod versus placebo. Compared with placebo, siponimod significantly reduced the risk of 3mCDP in participants aged ≥ 45 years (hazard ratio [HR]: 0.68; 95% confidence interval [CI]: 0.48-0.97), < 50 years (HR: 0.69; 95% CI: 0.49-0.98), ≥ 50 years (HR: 0.62; 95% CI: 0.40-0.96), and in participants with < 16 years DD (HR: 0.68; 95% CI: 0.47-0.98). The risk of 6mCDP was significantly reduced with siponimod versus placebo for participants aged < 45 years (HR: 0.60; 95% CI: 0.38-0.96), ≥ 45 years (HR: 0.67; 95% CI: 0.45-0.99), < 50 years (HR: 0.62; 95% CI: 0.43-0.90), and in participants with < 16 years DD (HR: 0.57; 95% CI: 0.38-0.87). Increasing age or longer MS duration did not appear to increase the risk of AEs, with an observed safety profile that remained consistent with the overall active SPMS and overall SPMS populations in EXPAND. CONCLUSIONS In participants with active SPMS, treatment with siponimod demonstrated a statistically significant reduction in the risk of 3mCDP and 6mCDP compared with placebo. Although not every outcome reached statistical significance in the subgroup analyses (possibly a consequence of small sample sizes), benefits of siponimod were seen across a spectrum of ages and DD. Siponimod was generally well tolerated by participants with active SPMS, regardless of baseline age and DD, and AE profiles were broadly similar to those observed in the overall EXPAND population.
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Affiliation(s)
- Le H Hua
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.
| | - Amit Bar-Or
- Department of Neurology and the Center for Neuroinflammation and Experimental Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stanley L Cohan
- Providence Multiple Sclerosis Center, Providence Brain and Spine Institute, Portland, OR, USA
| | - Fred D Lublin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia K Coyle
- Department of Neurology, Stony Brook University, Stony Brook, NY, USA
| | - Bruce Ac Cree
- UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Xiangyi Meng
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | - Wendy Su
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | | | - Robert J Fox
- Mellen Center for Multiple Sclerosis Treatment and Research Neurological Institute Cleveland Clinic, Cleveland, OH, USA
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Liu P, Ioannidis JPA, Ross JS, Dhruva SS, Luxkaranayagam AT, Vasiliou V, Wallach JD. Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study. BMC Med 2019; 17:188. [PMID: 31639007 PMCID: PMC6805640 DOI: 10.1186/s12916-019-1420-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 09/04/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. METHODS For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P < 0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). RESULTS Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P < 0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. CONCLUSION Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.
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Affiliation(s)
- Patrick Liu
- Yale School of Medicine, New Haven, CT, 06510, USA
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, Stanford, CA, 94305, USA.,Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, 94305, USA
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation (CORE), Yale-New Haven Health System, New Haven, CT, 06510, USA.,Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06510, USA.,National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, 06510, USA.,Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Sanket S Dhruva
- Department of Medicine, University of California, San Francisco School of Medicine, San Francisco, USA.,Section of Cardiology, San Francisco Veterans Affairs Health Care System, San Francisco, CA, 94121, USA
| | | | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Joshua D Wallach
- Center for Outcomes Research and Evaluation (CORE), Yale-New Haven Health System, New Haven, CT, 06510, USA. .,Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06510, USA. .,Collaboration for Research Integrity and Transparency (CRIT), Yale Law School, New Haven, CT, 06510, USA.
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