What Is Causing the Reduced Drug-Placebo Difference in Recent Schizophrenia Clinical Trials and What Can be Done About It?
Bibliographic record
Abstract
On September 18, 2007, a collaborative session between the International Society for CNS Clinical Trials and Methodology and the International Society for CNS Drug Development was held in Brussels, Belgium. Both groups, with membership from industry, academia, and governmental and nongovernmental agencies, have been formed to address scientific, clinical, regulatory, and methodological challenges in the development of central nervous system therapeutic agents. The focus of this joint session was the apparent diminution of drug-placebo differences in recent multicenter trials of antipsychotic medications for schizophrenia. To characterize the nature of the problem, some presenters reported data from several recent trials that indicated higher rates of placebo response and lower rates of drug response (even to previously established, comparator drugs), when compared with earlier trials. As a means to identify the possible causes of the problem, discussions covered a range of methodological factors such as participant characteristics, trial designs, site characteristics, clinical setting (inpatient vs outpatient), inclusion/exclusion criteria, and diagnostic specificity. Finally, possible solutions were discussed, such as improving precision of participant selection criteria, improving assessment instruments and/or assessment methodology to increase reliability of outcome measures, innovative methods to encourage greater subject adherence and investigator involvement, improved rater training and accountability metrics at clinical sites to increase quality assurance, and advanced methods of pharmacokinetic/pharmacodynamic modeling to optimize dosing prior to initiating large phase 3 trials. The session closed with a roundtable discussion and recommendations for data sharing to further explore potential causes and viable solutions to be applied in future trials.
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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 | 0.416 | 0.555 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".