Do clinical trials prepare to fail by failing to prepare? An examination of MS trials and recommendations for patient-reported outcome measure selection
Bibliographic record
Abstract
BACKGROUND: Many clinical trials use patient-reported outcome (PRO) measures, which can influence treatment decision-making, drug approval and label claims. Given that many PRO measure options exist, and there are conceptual and contextual complexities with PRO measurement, we aimed to evaluate how and why specific PRO measures have been selected for pivotal multiple sclerosis (MS) clinical trials. Specifically, we aimed to identify the reasons documented for PRO measure selection in contemporary phase III MS disease-modifying treatment (DMT) clinical trials. METHODS: We searched for phase III clinical trials of MS DMTs published between 2015 and 2021 and evaluated trial protocols, or primary publications where available, for PRO measure selection information. Specifically, we examined study documents for their clarification of clinical concepts measured, definitions of concepts measured, explanations of which PRO measures were considered, why specific PRO measures were chosen, and trade-offs in PRO measure selection. RESULTS: We identified 1705 abstracts containing 61 unique phase III MS DMT clinical trials. We obtained and examined 27/61 trial protocols. Six protocols were excluded: four contained no mention of PRO measures and two contained redacted sections preventing adequate assessment, leaving 21 protocols for assessment. For the remaining 34 trials (61-27), we retrieved 31 primary publications; 15 primary publications mentioned the use of a PRO measure. None of the 36 clinical trials that mentioned the use of PRO measures (21 protocols and 15 primary publications) documented clear PRO or clinical outcome assessment (COA) measurement strategies, provided clear justifications for PRO selection, or reasons why specific PRO measures were selected when alternatives existed. CONCLUSION: PRO measure selection for clinical trials is not evidence-based or underpinned by structured systematic approaches. This represents a critical area for study design improvement as PRO measure results directly affect patient care, PRO measurement has conceptual and contextual complexities, and there is a wide range of options when selecting a PRO measure. We recommend trial designers use formal approaches for PRO measure selection to ensure PRO measurement-based decisions are optimised. We provide a simple, logical, five-stage approach for PRO measure selection in clinical trials.
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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 | 0.823 | 0.923 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.035 | 0.037 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".