Concept Architecture
Concept
Theoretically, Network Meta-Analysis HTA is the application of network meta-analysis within health technology assessment (HTA) to estimate the comparative clinical effectiveness of multiple competing healthcare interventions. It integrates direct and indirect evidence from a connected treatment network to support reimbursement, pricing and policy decisions where multiple treatment options exist. The method exists to provide a coherent evidence base for economic evaluation when complete head-to-head clinical evidence is unavailable.
Mathematically, Network Meta-Analysis HTA applies the same statistical framework as conventional network meta-analysis, estimating relative treatment effects under the assumptions of similarity, homogeneity and consistency. Bayesian and frequentist models are both widely accepted, with random-effects models commonly used to account for between-study heterogeneity. The resulting comparative treatment effects and associated uncertainty are propagated into health economic models.
In practice, Network Meta-Analysis HTA is routinely undertaken as part of submissions to HTA agencies such as NICE, CADTH and PBAC. Comparative treatment-effect estimates generated from the analysis are incorporated into cost-effectiveness, cost-utility and budget impact models, while consistency assessments, heterogeneity analyses and sensitivity analyses are performed to demonstrate the robustness of the evidence supporting reimbursement decisions.
Purpose
Used to estimate comparative clinical effectiveness across multiple interventions for health technology assessment and to provide evidence inputs for reimbursement decisions and health economic evaluation.
Mathematical Formulae
Primary Formula
Consistency equation:
dAB = dAC ? dBC
where:
- dAB = treatment effect for A versus B
- dAC = treatment effect for A versus common comparator C
- dBC = treatment effect for B versus common comparator C
Supporting Formulae
Random-effects model:
?? ~ N(d, ��)
Random-effects weight:
w? = 1 / (Var(???) + ��)
Pooled estimate:
d? = (?w?d??) / ?w?
Related Mathematical Methods
- Network Meta-Analysis
- Mixed Treatment Comparison
- Bayesian Meta-Analysis
- Frequentist Meta-Analysis
- Indirect Comparison
- Node-Splitting
- Meta-Regression
- Cost-Effectiveness Analysis
Example
An HTA submission evaluates six biologic therapies for severe psoriasis. Only some treatments have been compared directly in randomised trials, so a random-effects network meta-analysis estimates all relative treatment effects across the connected evidence network. These estimates are incorporated into a Markov cost-effectiveness model submitted to NICE to determine the most cost-effective intervention.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| SUMPRODUCT | =SUMPRODUCT(B2:B30,C2:C30)/SUM(B2:B30) | Calculate weighted treatment-effect estimates |
| SUM | =SUM(B2:B30) | Calculate total study weights |
| SQRT | =SQRT(A2+B2) | Calculate standard errors for indirect comparisons |
| EXP | =EXP(A2) | Convert pooled log estimates to odds ratios, hazard ratios or risk ratios |
| Solver | Objective: minimise model deviance | Estimate treatment effects under network consistency constraints |
VBA (Optional)
Automate preparation of HTA evidence networks and export comparative treatment-effect estimates for incorporation into health economic models.
Sources
- NICE Decision Support Unit. Technical Support Documents: Evidence Synthesis for Decision Making.
- Dias S, Welton NJ, Sutton AJ, Ades AE. NICE Decision Support Unit Technical Support Documents.
- Chaimani A, Caldwell DM, Li T, et al. Chapter 11: Undertaking Network Meta-Analyses. Cochrane Handbook for Systematic Reviews of Interventions.
- NICE. Health Technology Evaluation Manual.
- ISPOR Good Practice Task Force Report on Network Meta-Analysis.
Related Concepts (2)
Library
Publications
1
Cochrane Handbook for Systematic Reviews of Interventions — Higgins, Thomas, Chandler, Cumpston, Li, Page & Welch, 2nd Edition ed., 2019 (John Wiley & Sons / Cochrane)
The standard guide to planning, conducting, interpreting and reporting systematic reviews of health interventions, with extensive material on meta-analysis, network meta-analysis, risk of bias, GRADE, equity, complex interventions and economics evidence. Maintained as a living online resource.
BookView source →
Frequently Asked Questions (6)
What is network meta-analysis in HTA?
The application of network meta-analysis specifically within health technology assessment, generating comparative effectiveness estimates when treatments were never studied together.
Source: Dias et al. 2013
Why is network meta-analysis needed in health technology assessment?
In health technology assessment a new treatment often must be compared with several existing options that it was never trialled against directly, and network meta-analysis is what makes that comparison possible. By combining the available trials into one network, it produces estimates of the new treatment's effect relative to each comparator, which the assessment needs to judge its value. These estimates then feed the economic model that weighs cost against benefit. Supplying comparisons the trials never made is its role here. Dias and colleagues (2013) describe this use.
Source: Dias et al. 2013
Why is network meta-analysis important in HTA?
Network meta-analysis is important in health technology assessment because assessments typically require comparing a new treatment with all relevant alternatives, yet direct head-to-head trials against every comparator are usually unavailable, so network meta-analysis provides the relative effectiveness estimates by combining direct and indirect evidence. These estimates are central to judging comparative value and feed into cost-effectiveness models. Without network meta-analysis, many needed comparisons could not be made. So network meta-analysis is important in HTA for generating the comparative effectiveness evidence on which assessments and their economic models depend, enabling comparison across the full set of relevant treatments.
Source: Dias et al. 2013
How does network meta-analysis support HTA decisions?
Network meta-analysis supports health technology assessment decisions by providing estimates of the relative effectiveness of a technology against all relevant comparators, including those not directly studied, together with the uncertainty in these estimates, which inform judgements of comparative value and feed into economic models of cost-effectiveness. It also allows treatments to be ranked. The synthesised estimates and their uncertainty are propagated through the model. So network meta-analysis supports HTA decisions by supplying the comparative effectiveness inputs and their uncertainty needed to assess a technology against its alternatives and to estimate cost-effectiveness across the relevant treatment options.
Source: Drummond et al. 2015
What challenges arise in network meta-analysis for HTA?
Challenges in network meta-analysis for HTA include ensuring the network is connected and the trials sufficiently comparable for transitivity to hold; assessing and addressing inconsistency between direct and indirect evidence; handling heterogeneity and sparse networks where some comparisons rest on little evidence; conveying the uncertainty appropriately to the economic model; and the assumptions and expertise the methods require. These challenges mean network meta-analysis for HTA is conducted rigorously, with attention to the network structure, the validity of the assumptions, and the propagation of uncertainty, so that the comparative estimates informing the assessment are as reliable and transparent as the evidence allows.
Source: Dias et al. 2013
How are network meta-analysis results used in economic models?
Network meta-analysis results are used in economic models by providing the relative effectiveness parameters for the treatments compared, such as the effects of each treatment relative to a common comparator, along with their uncertainty, which are entered into the model and propagated through it to estimate costs and outcomes. Using synthesised network estimates allows the model to compare all relevant treatments coherently, including those without direct trials. So network meta-analysis supplies the comparative effectiveness inputs, with their uncertainty, that economic models use, linking the combined clinical evidence across the treatment network to the estimation of cost-effectiveness in health technology assessment.
Source: Dias et al. 2013
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 3 Dec 2025
Content version: 1.0.0
Canonical Identity
- Term code
- HE-ES-ESM-041
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