The Importance of Considering Differences in Study Design in Network Meta-analysis: An Application Using Anti-Tumor Necrosis Factor Drugs for Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Adaptive trial designs present a methodological challenge when performing network meta-analysis (NMA), as data from such adaptive trial designs differ from conventional parallel design randomized controlled trials (RCTs). We aim to illustrate the importance of considering study design when conducting an NMA. METHODS: Three NMAs comparing anti-tumor necrosis factor drugs for ulcerative colitis were compared and the analyses replicated using Bayesian NMA. The NMA comprised 3 RCTs comparing 4 treatments (adalimumab 40 mg, golimumab 50 mg, golimumab 100 mg, infliximab 5 mg/kg) and placebo. We investigated the impact of incorporating differences in the study design among the 3 RCTs and presented 3 alternative methods on how to convert outcome data derived from one form of adaptive design to more conventional parallel RCTs. RESULTS: Combining RCT results without considering variations in study design resulted in effect estimates that were biased against golimumab. In contrast, using the 3 alternative methods to convert outcome data from one form of adaptive design to a format more consistent with conventional parallel RCTs facilitated more transparent consideration of differences in study design. This approach is more likely to yield appropriate estimates of comparative efficacy when conducting an NMA, which includes treatments that use an alternative study design. CONCLUSIONS: RCTs based on adaptive study designs should not be combined with traditional parallel RCT designs in NMA. We have presented potential approaches to convert data from one form of adaptive design to more conventional parallel RCTs to facilitate transparent and less-biased comparisons.
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 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 | 0.458 | 0.737 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".