Meta-analysis and Network Meta-analysis Services
Advanced Evidence Synthesis for Real-World Evidence (RWE) and HEOR Research
In today’s healthcare landscape, decision-making is increasingly driven by comprehensive, high-quality evidence. Meta-analysis and Network Meta-analysis (NMA) are essential tools in Health Economics and Outcomes Research (HEOR) and Real-World Evidence (RWE) generation, enabling researchers, policymakers, and industry leaders to compare treatment options, assess clinical and economic outcomes, and optimize healthcare strategies.
At Clievi, we specialize in conducting rigorous, methodologically sound meta-analyses and NMAs that adhere to international standards such as PRISMA, Cochrane Handbook, NICE, ISPOR, and GRADE. Our expertise in systematic evidence synthesis, comparative effectiveness research (CER), and Bayesian/Frequentist statistical modeling allows us to provide actionable insights for pharmaceutical companies, healthcare organizations, and regulatory agencies.
Our Meta-analysis and Network Meta-analysis Services
1. Systematic Meta-analysis for Comparative Effectiveness Research (CER)
Meta-analysis involves the statistical pooling of data from multiple studies to provide a more precise estimate of treatment effects. Our approach includes:
- Systematic Literature Review (SLR) and Study Selection – Conducting comprehensive database searches (PubMed, Embase, Cochrane, Scopus, Web of Science, ClinicalTrials.gov, and HTA repositories) to identify relevant studies.
- Data Extraction and Standardization – Using structured data extraction templates for population, intervention, comparator, outcomes (PICO framework), study design, and endpoints.
- Heterogeneity Assessment – Evaluating study variability using Cochran’s Q, I² statistics, and subgroup analyses.
- Fixed-Effects and Random-Effects Modeling – Conducting both DerSimonian-Laird random-effects models and Mantel-Haenszel fixed-effects models to account for between-study differences.
- Sensitivity and Subgroup Analyses – Identifying potential sources of bias and conducting stratified analyses for patient subgroups.
- Publication Bias Assessment – Using Egger’s test, Begg’s funnel plot, and Trim-and-Fill method to detect and adjust for bias.
- GRADE Framework for Evidence Quality – Evaluating certainty of evidence based on risk of bias, inconsistency, indirectness, imprecision, and publication bias.
2. Network Meta-analysis (NMA) for Multi-Treatment Comparisons
Network Meta-analysis (NMA), also known as Mixed Treatment Comparisons (MTC), allows for the indirect comparison of multiple interventions by synthesizing evidence from both direct and indirect comparisons. Our services include:
- Development of Evidence Networks – Structuring treatment comparisons into a coherent network model.
- Bayesian and Frequentist NMA Modeling – Conducting Bayesian hierarchical modeling using Markov Chain Monte Carlo (MCMC) methods and Frequentist NMA via meta-regression models.
- Consistency and Inconsistency Testing – Applying node-splitting analysis, Bucher’s indirect comparison method, and inconsistency models to ensure network reliability.
- Surface Under the Cumulative Ranking Curve (SUCRA) Analysis – Ranking treatment options based on effectiveness and safety.
- Sensitivity and Covariate Adjustments – Performing analyses to control for confounders and effect modifiers.
- League Tables and Forest Plots – Presenting comparative efficacy and safety results with credible intervals (CrIs) and confidence intervals (CIs).
- Regulatory and HTA Submission Compliance – Ensuring adherence to NICE, CADTH, ICER, PBAC, and FDA guidelines.
3. Advanced Statistical Methods for Meta-analysis and NMA
Our team employs cutting-edge statistical and machine learning techniques to enhance meta-analysis and NMA accuracy:
- Bayesian Meta-regression Modeling – Utilizing hierarchical Bayesian models for complex evidence networks.
- Multivariate Meta-analysis – Synthesizing multiple correlated outcomes simultaneously.
- Time-to-Event (Survival) Meta-analysis – Pooling hazard ratios (HRs) for progression-free survival (PFS) and overall survival (OS).
- Propensity Score and Matching-Adjusted Indirect Comparisons (MAICs) – Incorporating real-world evidence with randomized trial data.
- Individual Participant Data (IPD) Meta-analysis – Conducting patient-level data synthesis for enhanced precision.
Applications of Meta-analysis and NMA in RWE and HEOR
Our services support a wide range of healthcare and pharmaceutical applications, including:
- Comparative Effectiveness Research (CER) – Evaluating the relative benefits and risks of different treatment options.
- Health Technology Assessment (HTA) Submissions – Providing evidence for cost-effectiveness and budget impact models.
- Pharmacoeconomics and Market Access – Supporting payer negotiations and reimbursement decisions.
- Evidence-Based Clinical Guidelines – Informing clinical decision-making and treatment protocols.
- Regulatory Dossier Development – Strengthening FDA, EMA, and NICE submissions with robust evidence synthesis.
- Post-Marketing and Real-World Evidence (RWE) Generation – Supporting long-term safety and effectiveness evaluations.
- Medical Device and Digital Health Assessments – Comparing diagnostic and therapeutic technologies.
Key Databases and Sources We Utilize
We extract and analyze data from a wide range of sources, ensuring comprehensive evidence synthesis:
- Biomedical Literature – PubMed, Embase, Cochrane Library, Web of Science
- Clinical Trials Registries – ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
- HTA and Regulatory Reports – NICE, CADTH, ICER, PBAC, HAS, EMA, FDA
- Real-World Data (RWD) Sources – Electronic Health Records (EHRs), Insurance Claims, Patient Registries
- Health Economics and Outcomes Research (HEOR) Repositories – ISPOR, Tufts CEA Registry
Why Choose Clievi for Meta-analysis and NMA?
- Expertise in RWE and HEOR Evidence Synthesis – Our team includes biostatisticians, epidemiologists, and health economists.
- Compliance with Global Standards – We adhere to PRISMA, Cochrane, ISPOR, NICE, and FDA guidelines.
- State-of-the-Art Bayesian and Frequentist Modeling – Leveraging advanced meta-regression and network meta-analysis techniques.
- AI-Powered Systematic Reviews – Utilizing natural language processing (NLP) and machine learning for rapid screening and data extraction.
- Customizable Meta-analysis and NMA Solutions – Tailored for pharmaceutical, medical device, regulatory, and HTA applications.
- Regulatory-Ready Reports – Delivering publication-quality outputs for regulatory and payer submissions.
Get in Touch
Partner with Clievi for high-impact Meta-analysis and Network Meta-analysis services that drive Real-World Evidence (RWE) and Health Economics and Outcomes Research (HEOR).
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Website: www.clievi.com