The Ethics and Science of Placebo-Controlled Trials: Assay Sensitivity and the Duhem–Quine Thesis
Bibliographic record
Abstract
The principle of clinical equipoise requires that, aside from certain exceptional cases, second generation treatments ought to be tested against standard therapy. In violation of this principle, placebo-controlled trials (PCTs) continue to be used extensively in the development and licensure of second-generation treatments. This practice is typically justified by appeal to methodological arguments that purport to demonstrate that active-controlled trials (ACTs) are methodologically flawed. Foremost among these arguments is the so called assay sensitivity argument. In this paper, I take a closer look at this argument. Following Duhem, I argue that all trials, placebo-controlled or not, rely on external information for their meaningful interpretation. Pending non-circular empirical evidence that we can trust the findings of PCTs to a greater degree than the findings of ACTs, I conclude that the assay sensitivity argument fails to demonstrate that placebo-controlled trials are preferable, methodologically or otherwise, to active-controlled trials. Contrary to the intentions of its authors, the fundamental lesson taught by the assay sensitivity argument is Duhemian: the validity of all clinical trials depends on external information.
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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 | 0.373 | 0.453 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.089 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".