Analyzing Symptom Management Trials: The Value of Both Intention-to-Treat and Per-Protocol Approaches
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Two analytical approaches are described for a randomized trial testing interventions for symptom management. DESIGN: To compare an intention-to-treat with a perprotocol approach. SETTING: Patients were accrued from six cancer centers. SAMPLE: 94 men and 140 women with solid tumors were accrued. METHODS: An intention-to-treat approach (as randomized) and per-protocol analyses (at least one symptom reaching threshold and one follow-up intervention) were compared. The analysis determines how each approach affects results. A two-arm, six-contact, eight-week trial was implemented. In one arm, nurses followed a cognitive behavioral protocol. In the second arm, a non-nurse coach referred patients to a symptom management guide. MAIN RESEARCH VARIABLES: Trial arm; summed severity scores; interference-based severity categories at intake, 10 weeks, and 16 weeks; site; and stage of cancer. FINDINGS: Each arm produced a reduction in severity at 10 and 16 weeks with no differences between arms. In the per-protocol analyses, symptoms reported at the first contact required more time to resolve. Older patients exposed to the nurse arm resolved in fewer contacts. CONCLUSIONS: The intention-to-treat analyses indicated that both arms were successful but offered few insights into how symptoms or patients influenced severity. Per-protocol analyses (intervention and dose), when, and which strategies affected symptoms. IMPLICATIONS FOR NURSING: Each analytical strategy serves a purpose. Intention-to-treat defines the success of a trial. Per-protocol analyses allow nurses to pose clinical questions about response and dose of the intervention. Nurses should participate in analyses of interventions to understand the conditions where interventions are successful.
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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 | 0.789 | 0.846 |
| Meta-epidemiology (narrow) | 0.010 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.020 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 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".