Disconnected by design: analytic approach in treatment networks having no common comparator
Bibliographic record
Abstract
In a network meta-analysis, comparators of interest are ideally connected either directly or via one or more common comparators. However, in some therapeutic areas, the evidence base can produce networks that are disconnected, in which there is neither direct evidence nor an indirect route for comparing certain treatments within the network. Disconnected networks may occur when there is no accepted standard of care, when there has been a major paradigm shift in treatment, when use of a standard of care or placebo is debated, when a product receives orphan drug designation, or when there is a large number of available treatments and many accepted standards of care. These networks pose a challenge to decision makers and clinicians who want to estimate the relative efficacy and safety of newly available agents against alternatives. A currently recommended approach is to insert a distribution for the unknown treatment effect(s) into a network meta-analysis model of treatment effect. In this paper, we describe this approach along with two alternative Bayesian models that can accommodate disconnected networks. Additionally, we present a theoretical framework to guide the choice between modeling approaches. This paper presents researchers with the tools and framework for selecting appropriate models for indirect comparison of treatment efficacies when challenged with a disconnected framework. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.233 | 0.430 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".