nma-methodology

# Network Meta-Analysis Methodology Comprehensive methodological guidance for conducting rigorous network meta-analysis following NICE DSU and PRISMA-NMA guidelines. ## When to Use This Skill - Planning a network meta-analysis - Assessing transitivity and consistency - Interpreting treatment rankings - Choosing between frequentist and Bayesian NMA - Designing NMA sensitivity analyses - Reviewing NMA code or results ## Fundamental Assumptions ### 1. Transitivity Assumption **Definition**: If we can estimate A vs B directly and B vs C directly, we can estimate A vs C indirectly, provided the studies are sufficiently similar. **Requirements**: - Studies comparing different treatments should be similar enough to have been included in the same RCT - Effect modifiers should be balanced across comparisons - No important differences in study-level characteristics **Assessment**: ``` For each comparison in network, check: ├── Population similarity │ - Age, sex, disease severity │ - Biomarker status, prior treatments ├── Outcome definitions │ - Same definition of response/event │ - Same time point of assessment ├── Treatment definitions │ - Dose, duration, route │ - Concomitant medications └── Study design - Randomization, blinding - Follow-up duration ``` **Presenting Transitivity Assessment**: - Create table of study characteristics by comparison - Highlight any systematic differences - Use forest plots stratified by comparison ### 2. Consistency Assumption **Definition**: Direct and indirect evidence for the same comparison should agree (within random variability). **Relation to Transitivity**: - Transitivity is untestable (conceptual) - Consistency is testable (statistical) - Consistency violations suggest transitivity violations ## Consistency Assessment ### Global Consistency Tests #### Design-by-Treatment Interaction ```r # netmeta decomp.design(nma_result) # Tests overall consistency across network # Q statistic partitioned into within-design and between-design ``` #### Q Statistic Decomposition - Q_total = Q_heterogeneity + Q_inconsistency - Test Q_inconsistency against chi-square distribution ### Local Consistency: Node-Splitting ```r # netmeta netsplit(nma_result) # gemtc nodesplit_model <- mtc.nodesplit(network) ``` **Interpretation**: | Direct vs Indirect | Conclusion | |-------------------|------------| | Similar (p > 0.05) | No evidence of inconsistency | | Different (p < 0.05) | Possible inconsistency - investigate | **Caution**: Multiple testing - expect some false positives. ### Net Heat Plot ```r netheat(nma_result) # Visual display of inconsistency # Red: high inconsistency contribution # Blue: low inconsistency ``` ### What to Do with Inconsistency 1. **Check data** - errors in data entry 2. **Investigate sources** - which comparisons differ 3. **Explore heterogeneity** - meta-regression on potential modifiers 4. **Consider splitting network** - if clinical rationale exists 5. **Report transparently** - don't hide inconsistency 6. **Use inconsistency model** - as sensitivity analysis ## Treatment Rankings ### Frequentist (netmeta) #### P-scores ```r netrank(nma_result, small.values = "bad") # P-score: probability of being better than average treatment # Ranges 0-1 # NOT probability of being best ``` ### Bayesian (gemtc) #### SUCRA (Surface Under Cumulative Ranking Curve) ```r sucra(mtc_result) # Similar interpretation to P-score # Based on cumulative ranking probabilities ``` #### Probability of Being Best ```r rank.probability(mtc_result) # Full ranking probability matrix # Prob_best = P(rank = 1) ``` ### Interpretation Cautions **Critical**: Rankings are uncertain - always present with uncertainty measures. ``` Problems with rankings: ├── Small differences → different rankings ├── Wide credible intervals often ignored ├── Multiple treatments may be effectively tied ├── Rankings don't consider clinical relevance └── "Best" might have limited evidence ``` **Best Practice**: - Report ranking probabilities, not just point ranks - Show cumulative ranking plots - Consider clustering treatments by effect - Discuss clinical significance alongside statistical ## Model Selection ### Fixed vs Random Effects | Factor | Fixed-Effect | Random-Effects | |--------|-------------|----------------| | Studies similar | ✓ | ✓ | | Studies different | ✗ | ✓ | | Few studies per comparison | Consider | Default | | Inference goal | Included studies | Broader population | ### Bayesian Prior Selection #### Treatment Effects ```r prior_trt = normal(0, sd) # sd should be large enough to be weakly informative # Consider scale of effect measure (log OR ~2-3 is large) ``` #### Heterogeneity (τ) ```r prior_het = half_normal(scale) # Scale depends on expected heterogeneity # Turner et al. informative priors available ``` ### Model Comparison #### DIC (Deviance Information Criterion) ```r # Lower is better # Difference of ~3-5 is meaningful dic(model1) dic(model2) ``` #### Residual Deviance - Compare to number of data points - Should be close if model fits well ## Network Geometry ### Key Considerations ``` Network Structure Assessment: ├── Connectivity │ - All treatments connected (directly or indirectly)? │ - Star network? (single common comparator) │ - Well-connected? ├── Evidence Distribution │ - Some comparisons well-informed, others sparse? │ - Imbalanced networks problematic ├── Multi-arm Trials │ - Must account for correlations │ - Contribution to network └── Placebo/Active Control - Consider clinical relevance of network anchor ``` ### Contribution Matrix ```r # netmeta netcontrib(nma_result) # Shows % contribution of each direct comparison to each estimate ``` ### Network Graph ```r netgraph(nma_result, plastic = FALSE, thickness = "number.of.studies", multiarm = TRUE, points = TRUE) ``` ## Reporting Checklist (PRISMA-NMA) ### Methods - [ ] Network geometry description - [ ] Transitivity assessment approach - [ ] Effect measure and rationale - [ ] Model choice (fixed/random, frequentist/Bayesian) - [ ] Prior specifications (if Bayesian) - [ ] Consistency assessment methods - [ ] Ranking methods and interpretation - [ ] Sensitivity analyses planned ### Results - [ ] Network diagram - [ ] Study characteristics table by comparison - [ ] Pairwise MA results (for direct evidence) - [ ] NMA results for all comparisons - [ ] League table - [ ] Consistency assessment results - [ ] Treatment rankings with uncertainty - [ ] Sensitivity analysis results ## Common Pitfalls ### 1. Ignoring Transitivity - Must assess before running NMA - Not just a formality - fundamental requirement ### 2. Over-interpreting Rankings - "Treatment A ranked #1" without uncertainty - Small differences may give different rankings - Clinical relevance matters more than rank ### 3. Selective Consistency Reporting - Report all node-split results - Don't dismiss inconsistency findings ### 4. Multi-arm Trial Handling - Must account for correlations - Software handles this, but check it's done correctly ### 5. Sparse Networks - Very uncertain indirect comparisons - Consider if NMA is appropriate ## Quick Reference Code ### Frequentist (netmeta) ```r library(netmeta) # Fit NMA nma <- netmeta(TE, seTE, treat1, treat2, studlab, data = pairwise_data, sm = "OR", reference.group = "Placebo", random = TRUE) # Network graph netgraph(nma, plastic = FALSE, multiarm = TRUE) # Forest vs reference forest(nma, reference.group = "Placebo") # League table netleague(nma) # Consistency netsplit(nma) netheat(nma) # Rankings netrank(nma, small.values = "bad") ``` ### Bayesian (gemtc) ```r library(gemtc) library(rjags) # Create network network <- mtc.network(data.ab = arm_data) # Fit model model <- mtc.model(network, likelihood = "binom", link = "logit", linearModel = "random") result <- mtc.run(model, n.adapt = 5000, n.iter = 50000) # Check convergence gelman.diag(result) # Summary summary(result) # Rankings rank.probability(result) sucra(result) # Node-splitting nodesplit <- mtc.nodesplit(network) ns_result <- mtc.run(nodesplit) summary(ns_result) ``` ## Resources - NICE DSU TSD 2: https://www.sheffield.ac.uk/nice-dsu/tsds - PRISMA-NMA: Hutton et al. 2015 - Dias et al. (2018): Network Meta-Analysis for Decision Making - Salanti (2012): Ann Intern Med - Intro to NMA