Analysing complex interventions using component network meta-analysis
Bibliographic record
Abstract
Standfirst Systematic reviews with network meta-analysis (NMA) frequently evaluate complex interventions combining multiple healthcare interventions (known as components). Components may act separately of each other or in conjunction with other components, synergistically or antagonistically. Component effect estimation is crucial to produce relevant and clinically meaningful evidence. However, standard NMA cannot quantify individual component effects of complex interventions. This study presents methods for modeling complex interventions and highlights the advantages and limitations of component NMA (CNMA). CNMA enables the estimation of individual component effects, whether additive or interactive. Interaction CNMA can be considered an extension of the additive CNMA model that includes interaction terms. We give practical guidance on how to carry out these analyses via empirical examples, which showcase both the strengths and limitations of CNMA. Implementing CNMA models is complex and requires the skills of a multidisciplinary team including clinicians, methodologists, and statisticians. Summary points CNMA provides the opportunity to disentangle the effects of components of complex interventions and assess their efficacy or safety, accounting for potential interactions in component combinations. Under the additivity assumption, CNMA assumes that the total effect of a complex intervention is the sum of its individual component effects (e.g., if component A lowers a symptom score by 2 points and B by 1 point, A+B is expected to lower it by 3 points), while interaction CNMA is used when there is evidence of violation of additivity and the combined effect differs due to synergy or antagonism. Interaction CNMA can be considered a compromise between additive CNMA and standard NMA, but selecting interaction terms requires clinical, statistical, and methodological considerations. Clinicians and other knowledge users should be engaged in the selection of interaction CNMA models to ensure biological plausibility.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
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 | 0.109 | 0.232 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".