The Hawthorne Effect, Sponsored Trials, and the Overestimation of Treatment Effectiveness
Bibliographic record
Abstract
OBJECTIVE: To determine if the results of rheumatoid arthritis (RA) clinical trials are upwardly biased by the Hawthorne effect. METHODS: We studied 264 patients with RA who completed a commercially sponsored 3-month, open-label, phase 4 trial of a US Food and Drug Administration approved RA treatment. We evaluated changes in the Health Assessment Questionnaire disability index (HAQ) and visual analog scales for pain, patient global, and fatigue during 3 periods: pretreatment in the trial, on treatment at the close of the trial, and by a trial-unrelated survey 8 months after the close of the trial, but while the patients were receiving the same treatment. RESULTS: The HAQ score (0-3) improved by 41.3% during the trial, but only by 16.5% when the endpoint was the post-trial result. Similar results for the other variables were patient global (0-10) 51.9% and 34.6%, pain (0-10) 51.7% and 39.7%, fatigue (0-10) 45.6% and 24.6%. Worsening between the trial end and the first survey assessment was HAQ 0.29 units, pain 0.8 units, patient global 0.8 units, and fatigue 1.1 units. CONCLUSION: Almost half the improvement noted in the clinical trial HAQ score disappeared on entry to a non-sponsored followup study, and from 23% to 44% of improvements in pain, patient global, and fatigue also disappeared. These changes can be attributed to the Hawthorne effect. Based on these data, we hypothesize that the absolute values of RA outcome variables in clinical trials are upwardly biased, and that the treatment effect is less than observed.
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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 | 0.533 | 0.701 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".