Use of Bayesian techniques in clinical trials for rheumatoid arthritis and systemic sclerosis: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To gather all relevant literature surrounding the use of Bayesian methods in clinical trials for rheumatoid arthritis and systemic sclerosis; and to assess the use of these methods within said trials. METHODS: Medline and Embase were searched on August 18, 2024. The search strategy and screening process was performed by a single reviewer and verified by a secondary expert. We included studies that presented the primary results of a clinical trial designed to examine a treatment for either rheumatoid arthritis or systemic sclerosis, and that also included the use of a Bayesian technique. From these studies, we extracted the following information: author(s), title, year of publication, study objectives, disease under study, treatment under study, description of study sample, phase of trial, main results, description of Bayesian technique employed, and rationale for use of Bayesian technique (if applicable). The Cochrane risk of bias assessment tool was used to critically appraise each included study. Extracted data were recorded in a spreadsheet and results were synthesized narratively. RESULTS: A total of 11 studies were included in the final review. Seven of these studies evaluated treatments for rheumatoid arthritis, and four evaluated treatments for systemic sclerosis. A total of five Bayesian techniques were identified. These techniques included the use of posterior probabilities for efficacy analysis, simulation to estimate power and type I error for trials that employed a Bayesian analysis, Bayesian dose-finding algorithms, interim analyses using Bayesian stopping rules, and Bayesian response-adaptive randomization. A variety of rationales for the decision to use Bayesian methods were expressed. CONCLUSIONS: The application of Bayesian methods offers many notable advantages, yet their uptake within rheumatology trials has been slow. Increased awareness of these advantages could greatly benefit the clinical trial world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.739 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.026 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".