Evidence map›Paper›PMID 41940181›Full record

ArticleMultiple sclerosis journal - experimental, translational and clinical

Assessment of the impact of outcome definitions on power in multiple sclerosis clinical trials.

Cynthia X Li, Bonnie I Glanz, Moogeh Baharnoori, Brian C Healy

Abstract read
In one paragraph

Article in Multiple sclerosis journal - experimental, translational and clinical. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Cynthia X LiBrigham MS Center, Brigham and Women's Hospital, Boston, MA, USA.
Bonnie I GlanzBrigham MS Center, Brigham and Women's Hospital, Boston, MA, USA.
Moogeh BaharnooriBrigham MS Center, Brigham and Women's Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0002-3043-4294
Brian C HealyBrigham MS Center, Brigham and Women's Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0001-5272-2425

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Clinical trials for disease progression in multiple sclerosis (MS) often use a confirmed increase on the expanded disability status scale (EDSS) as the primary outcome. Objective: To compare potential outcome measures of disease progression for MS clinical studies. Methods: Data were simulated with clinical visits including measurement of the EDSS and relapses completed every 6 months. At each visit, the EDSS could increase due to a relapse, disease progression, or other reasons. Six definitions of disease progression were used based on the number of measurements required for the progression (1, 2, or 3) and whether concurrent relapse could have occurred. The power for each definition was calculated as the proportion of simulated datasets with a statistically significant treatment effect. Results: All the approaches maintained the type I error rate when there was no overall treatment effect. The approach with progression at a single visit and no relapses led to the greatest power when there was only a treatment effect on progression and there were no increases due to other causes, and this scenario may be directly applicable to a progressive MS trial. For other scenarios, the best approach included a confirmation visit. Conclusions: Choosing the best outcome definition for a clinical trial depends on the assumed mechanism of action of the treatment and the patient population.

Indexed as

Clinical trialsdisease progressionmultiple sclerosisprogression independent of relapse activitystatistical power

Identifiers

PMID41940181
PMCPMC13049325

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.