Incorporating alternative design clinical trials in network meta-analyses
Bibliographic record
Abstract
INTRODUCTION: Network meta-analysis (NMA) is an extension of conventional pairwise meta-analysis that allows for simultaneous comparison of multiple interventions. Well-established drug class efficacies have become commonplace in many disease areas. Thus, for reasons of ethics and equipoise, it is not practical to randomize patients to placebo or older drug classes. Unique randomized clinical trial designs are an attempt to navigate these obstacles. These alternative designs, however, pose challenges when attempting to incorporate data into NMAs. Using ulcerative colitis as an example, we illustrate an example of a method where data provided by these trials are used to populate treatment networks. METHODS: We present the methods used to convert data from the PURSUIT trial into a typical parallel design for inclusion in our NMA. Data were required for three arms: golimumab 100 mg; golimumab 50 mg; and placebo. Golimumab 100 mg induction data were available; however, data regarding those individuals who were nonresponders at induction and those who were responders at maintenance were not reported, and as such, had to be imputed using data from the rerandomization phase. Golimumab 50 mg data regarding responses at week 6 were not available. Existing relationships between the available components were used to impute the expected proportions in this missing subpopulation. Data for placebo maintenance response were incomplete, as all induction nonresponders were assigned to golimumab 100 mg. Data from the PURSUIT trial were combined with ACT-1 and ULTRA-2 trial data to impute missing information. DISCUSSION: We have demonstrated methods for converting results from alternative study designs to more conventional parallel randomized clinical trials. These conversions allow for indirect treatment comparisons that are informed by a wider array of evidence, adding to the precision of estimates.
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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 | 0.272 | 0.505 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".