Challenges in Using the Randomized Trial Design to Examine the Influence of Treatment Preferences
Bibliographic record
Abstract
The overall purpose of this methodological study was to investigate the strengths and limitations of the randomized clinical trial design in examining the influence of treatment preferences on outcomes. The study was a secondary analysis of data obtained in two randomized clinical trials that evaluated behavioral therapies for insomnia. In both trials, the same design and methods were used to assess participants' treatment preferences and outcomes, however, the treatments differed. The results illustrated the challenges encountered in using the randomized clinical trial design. The challenges were related to the unbalanced distribution of participants with preferences for the study treatments, non-comparability of the subgroups with treatments matched or mismatched to their preferences, differential attrition, which compromised the sample size and composition of the subgroups and limited the use of the planned statistical analyses. Whether these challenges occur in trials of other types of treatments and target populations should be explored in future research. Some strategies were proposed and should be evaluated for their utility in addressing these challenges.
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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 | 0.864 | 0.901 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".