Improving completion rates of patient-reported outcome measures in cancer clinical trials: Scoping review investigating the implications for trial designs
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) play a crucial role in cancer clinical trials. Despite the availability of validated PRO measures (PROMs), challenges related to low completion rates and missing data remain, potentially affecting the trial results' validity. This review explored strategies to improve and maintain high PROM completion rates in cancer clinical trials. METHODOLOGY: A scoping review was performed across Medline, Embase and Scopus and regulatory guidelines. Key recommendations were synthesized into categories such as stakeholder involvement, study design, PRO assessment, mode of assessment, participant support, and monitoring. RESULTS: The review identified 114 recommendations from 18 papers (16 peer-reviewed articles and 2 policy documents). The recommendations included integrating comprehensive PRO information into the study protocol, enhancing patient involvement during the protocol development phase and in education, and collecting relevant PRO data at clinically meaningful time points. Electronic data collection, effective monitoring systems, and sufficient time, capacity, workforce and financial resources were highlighted. DISCUSSION: Further research needs to evaluate the effectiveness of these strategies in various context and to tailor these recommendations into practical and effective strategies. This will enhance PRO completion rates and patient-centred care. However, obstacles such as patient burden, low health literacy, and conflicting recommendations may present challenges in application.
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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 | 0.326 | 0.687 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".