Comparative Effectiveness of Interventions for Multiple Long‑Term Conditions Using Network and Component Network Meta‑Analysis

Supervisors:

• Dr Ellesha Smith eas24@leicester.ac.uk
• Professor Laura Gray

Project Description:

Background

Multiple long‑term conditions (MLTC), or multimorbidity, is a growing public health challenge, affecting millions of people and placing substantial strain on healthcare systems. Despite increasing numbers of trials, the evidence base remains fragmented and inconclusive due to heterogeneity in populations, interventions, and outcomes. Existing systematic reviews have largely relied on pairwise meta‑analysis or narrative synthesis, limiting the ability to compare multiple interventions and identify optimal strategies.

Network meta‑analysis (NMA) offers a robust framework for simultaneously comparing multiple interventions and estimating their relative effectiveness. However, MLTC interventions are typically complex and multicomponent, meaning that evaluating them as a whole makes it difficult to identify the contribution of individual components. Component network meta‑analysis (CNMA) extends NMA by disentangling the effects of intervention components, allowing identification of the most effective components and combinations without requiring direct evidence for every possible combination.
Furthermore, heterogeneity across trials, in terms of population characteristics, intervention design, and context, remains a key barrier to meaningful synthesis. Understanding and explaining this heterogeneity is essential to improve the applicability of findings and address health inequalities.

Aim

To evaluate the effectiveness of interventions for people living with MLTC using evidence synthesis methods, and to identify how intervention components and study-level characteristics affect intervention effects.

Objectives

1. Update and extend an existing systematic review of MLTC intervention trials and extract relevant data.
2. Conduct NMA to estimate the comparative effectiveness of interventions across all included trials.
3. Conduct CNMA to identify the contribution of individual intervention components and their combinations.
4. Investigate heterogeneity across studies using meta-regression and subgroup analyses.

Methods

A structured evidence synthesis will be undertaken using aggregate trial data. The existing systematic review will be updated to identify eligible MLTC trials through comprehensive database and registry searches, followed by independent screening, data extraction, and risk of bias assessment. Intervention descriptions will be systematically coded into components to support subsequent analyses. NMA will be used to compare the effectiveness of interventions, applying random-effects models and assessing network structure, heterogeneity, and consistency. To address the complexity of MLTC interventions, CNMA will be undertaken to estimate the effects of individual components and their combinations, identifying which elements contribute most to effectiveness. Finally, heterogeneity in intervention effects will be explored using meta-regression and subgroup analyses, examining how study-level characteristics modify intervention effects and improve the interpretability and generalisability of findings.

Expected Outcomes and Impact

This project will deliver a comprehensive and up-to-date synthesis of evidence on interventions for people living with MLTC. It will generate new insights into the comparative effectiveness of interventions, the contribution of individual components within complex programmes, and the extent to which findings vary across different study contexts. By addressing key limitations in the current evidence base, the research will improve understanding of what works, and why, in the management of MLTC.
By identifying effective interventions and components, the findings have the potential to inform clinical decision-making and support the development of evidence-based guidelines. The work will also highlight gaps in the evidence and methodological challenges, informing the design of future trials and evidence synthesis. In doing so, it will contribute to improving the quality, relevance, and applicability of research in this area. Ultimately, the project aims to support more effective, efficient, and equitable approaches to managing MLTC, with the potential to improve health outcomes and quality of life for affected populations.

Training and Environment

The studentship will provide advanced training in systematic review methods, network meta-analysis, and component network meta-analysis, alongside broader skills in epidemiology and health data analysis. Supported by NIHR infrastructure, the project benefits from a strong interdisciplinary research environment with expertise in MLTC, evidence synthesis, and clinical trials. The student will also gain experience working within established research networks, with opportunities for collaboration, dissemination, and engagement to ensure the research has both methodological rigour and real-world relevance.

References 

Zhang L, Fisher E, Bradbury N, Darko N, Khunti K, Lock S, Singh SJ, Simpson SA, Smith E, Smith SM, Taylor RS. Randomised controlled trials for improving health outcomes for people living with multiple long-term conditions: Protocol for a systematic review of methodological approaches, risk of bias and reporting quality. Plos one. 2025 Jun 30;20(6):e0325742.

Smith SM, Wallace E, Clyne B, Boland F, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community setting: a systematic review. Systematic Reviews. 2021 Oct 20;10(1):271.

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