Collection of Post-treatment PRO Data in Oncology Clinical Trials
Bibliographic record
Abstract
As patient-reported outcome (PRO) measures are being included more frequently in oncology clinical trials, regulatory and health technology assessment agencies have begun to request long-term, post-treatment PRO data to supplement traditional survival/progression endpoints. These data may be collected as part of cohort extension or registry studies to describe long-term outcomes of study participants after concluding their cancer treatment. While post-treatment PRO data may be expected to satisfy regulatory and payer expectations, significant practical barriers exist for the efficient incorporation of these data into oncology clinical trials, such as subject attrition, protocol deviations, and treatment crossover. The incorporation of post-treatment PRO assessments is a resource-intensive task requiring clear objectives for how the data will be analyzed and interpreted by both sponsors and regulators. Incorporating PRO data collection via electronic modalities (e.g., smartphone, web) may be a less expensive and more feasible option for incorporating long-term follow-up, reducing the frequency of manual study staff follow-up and expensive clinic visits. It is essential to include well-defined estimands for the statistical analysis, as well as to document limitations associated with the long-term follow-up data-collection approach. Analytical techniques will likely rely on descriptive and model-based statistics, and conclusions about treatment differences will likely be limited to preliminary findings of effectiveness (instead of efficacy). Finally, communications with health authorities and regulatory agencies regarding the LTFU study design and analysis should occur as early as possible to ensure that the PRO data to be collected offer an opportunity to properly evaluate the research question(s) of interest.
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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 | 0.695 | 0.762 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".