Comparison of Patients Treated with Natalizumab and Interferon-beta/Glatiramer Using Propensity-Matched Multiple Sclerosis Registry Data (P01.211)
Bibliographic record
Abstract
OBJECTIVE: Investigate time to first multiple sclerosis (MS) relapse using matched patient samples from the contemporaneously recruited MSCOMET and TYSABRI ® Observational Program (TOP) cohorts. BACKGROUND: Comparison of treatments using nonrandomized data is difficult because imbalances in patient characteristics may introduce bias. Propensity score matching is a statistical technique used to correct for imbalance of known covariates in nonrandomly selected cohorts. DESIGN/METHODS: MSCOMET is a longitudinal MSBase registry substudy assessing patients treated with interferon beta (IFN) and glatiramer acetate (GA). TOP is a natalizumab observational registry. Currently, 694 patients from 14 countries (median follow-up time of 12 months) and 3976 patients from 15 countries (median follow-up time of 17 months) are enrolled in MSCOMET and TOP, respectively. Baseline characteristics used for 1:1 propensity matching included sex, age, disease duration, Expanded Disability Status Scale scores, and prebaseline treatment and relapse activity. Matching success was assessed via analysis of standardized differences, with the mean difference between matched groups expressed as a percentage of the average covariate standard deviation. Additional factors associated with time to first relapse were investigated using a clustered marginal Cox model. RESULTS: TOP patients (n=569) were matched to MSCOMET patients (n=569). For each variable the standardized difference was <10%, indicating excellent balance in all baseline characteristics. Among MSCOMET patients, relapse risk was increased 2.73-fold (95% CI, 2.10–3.55-fold) over TOP patients. In the nonmatched sample, relapse risk was 1.68-fold (95% CI, 1.10–2.19-fold) greater among MSCOMET than TOP patients. Within MSCOMET, relapse risk did not differ between GA- and IFN-treated patients. CONCLUSIONS: Natalizumab treatment was associated with a significantly reduced risk of relapse compared with IFN and GA in a contemporaneously and propensity matched comparison of patients across 2 observational studies. While inferior to randomized clinical trials, the propensity score matching technique could be useful when head-to-head randomized trial evidence is lacking. Supported by: Biogen Idec Inc. and Elan Pharmaceuticals, Inc. Disclosure: Dr. Spelman hs received research support from Novartis. Dr. Pellegrini has received personal compensation for activities with Biogen Idec. Dr. Zhang has received personal compensation for activities with Biogen Idec Inc. as an employee. Dr. Zhang holds stock and/or stock options in Biogen Idec. Dr. Zhang has received research support from Biogen Idec. Dr. Hyde has received personal compensation for activities with Biogen Idec as an employee. Dr. Hyde has received compensation for serving as international medical affairs director of Biogen Idec. Dr. Hyde holds stock and/or stock options in Biogen Idec. Dr. Pace receives personal compensation from Biogen Idec Inc. as an employee. Dr. Pace receives stock from Biogen Idec Inc. Dr. Belachew has received personal compensation for activities with Biogen Idec as an employee. Dr.Trojano has received personal compensation for activities with Sanofi-Aventis Pharmaceuticals, Inc., Biogen Idec, Novartis, and Bayer Schering. Dr. Trojano has received research supprot from Merck Serono, Biogen Idec, and Novartis. Dr. Wiendl has received personal compensation for activities with Bayer, Biogen Idec, Elan Corporation, Medac, Merck Serono, Novo Nordisk, Sanofi-Aventis Pharmaceuticals, Inc., Schering, Teva Neuroscience, Bayer Vital/Schering, and Novartis. Dr. Wiendl has received research support from Bayer, Biogen Idec, Elan Corporation, Medac, Merck Serono, Novo Nordisk, Medac, Sanofi-Aventis Pharmaceuticals, Inc., Schering, and Teva Neuroscience. Dr. Kappos has receied personal compensation for activities with Actelion, Advancell, Allozyne, BaroFold, Bayer Health Care Pharmaceuticals, Bayer Schering Pharma, Bayhill, Biogen Idec, BioMarin, CLC Behring, Elan, Genmab, Genmark, GeNeuro SA and GlaxoSmithKline. Dr. Kappos has received research support from has received research support from Acorda, Actelion, Allozyne, BaroFold, Bayer HealthCare, Bayer Schering, Bayhill Therapeutics, Biogen Idec, Boehringer Ingelheim, Eisai, Elan, Genmab, GlaxoSmithKline, Glenmark, Merck Serono, MediciNova and Nova. Dr. Verheul has received personal compensation for activities with Merck Serono, Biogen Idec, and Novartis. Dr. Grand-Maison has received personal compensation for activities with Sanofi-Aventis Pharmaceuticals, Inc., Bayer Pharmaceuticals, Serono Inc., Biogen Idec, Genzyme Corporation, and Novartis. Dr. Grand-Maison has received research support from Sanofi-Aventis Pharmaceuticals, Inc., Serono Inc., Biogen Idec, Genzyme Corporation, Bayer Pharmaceuticals, and Novartis. Dr. Izquierdo has received personal compensation for activities with Biogen Idec, Merck & Co., Inc., Bayer, Sanofi-Aventis Pharmaceuticals, Inc., Teva Neuroscience, and Novartis. Dr. Butzkueven has received personal compensation for activities with Biogen Idec, Novartis for scientific advisory boards, from Novartis, CSL (Australia) as a speaker, conference travel support from Biogen Idec, Novartis. Dr. Butzkueven has received research support from Merck Serono, Biogen Idec, Novartis. Dr. Spelman hs received research support from Novartis. Dr. Spelman hs received research support from Novartis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".