{"id":6724,"date":"2026-05-07T12:18:25","date_gmt":"2026-05-07T10:18:25","guid":{"rendered":"https:\/\/mediconomics.com\/glossar\/evidence-synthesis\/"},"modified":"2026-05-07T12:18:25","modified_gmt":"2026-05-07T10:18:25","slug":"evidence-synthesis","status":"publish","type":"glossary","link":"https:\/\/mediconomics.com\/en\/glossar\/evidence-synthesis\/","title":{"rendered":"Evidence Synthesis"},"content":{"rendered":"

Evidence Synthesis<\/strong> (German: evidence synthesis) refers to methods used to consolidate existing scientific findings into a clear, transparent overall conclusion. In the development of pharmaceuticals and medical devices, evidence synthesis is used to assess efficacy, safety, and benefit\u2013risk aspects based on multiple sources rather than relying on a single study. The term covers both quantitative methods (e.g., meta-analysis) and qualitative or structured approaches (e.g., narrative synthesis) and forms the basis of many regulatory and HTA-relevant dossiers. <\/p>\n

What is Evidence Synthesis used for in practice?<\/h2>\n

In clinical development, evidence synthesis supports study planning, the interpretation of conflicting results, and the derivation of evidence-based decisions. Typical use cases include deriving clinically relevant endpoints, contextualising the effect size against the current standard of care, and substantiating why certain populations or comparator arms are appropriate. In rare diseases (orphan drugs) or small patient cohorts, systematically consolidating external evidence can also help to make knowledge gaps transparent and justify additional data sources. <\/p>\n

In Europe, evidence synthesis is also closely linked to requirements from Health Technology Assessment (HTA) and benefit assessment. For marketing authorisation and reimbursement decisions, sponsors and manufacturers often need to demonstrate how the totality of evidence supports the claimed benefit, which uncertainties remain, and how these should be addressed (e.g., through additional studies or post-authorisation measures). In early-access settings, a robust synthesis can also help to describe the plausibility and limitations of external data transparently. <\/p>\n

Main types: quantitative and qualitative synthesis<\/h2>\n

Quantitative evidence synthesis<\/strong> includes methods such as meta-analyses, in which results from multiple studies are statistically combined. The aim is a more precise estimate of the effect size (e.g., hazard ratio or risk difference) and an exploration of heterogeneity between studies. In practice, studies must be sufficiently comparable, and data preparation follows predefined rules to minimise bias. <\/p>\n

Qualitative or structured synthesis<\/strong> is used when statistical pooling is not meaningful, for example due to major differences in design, populations, or endpoints. Here, results are consolidated within a transparent framework, including assessment of study quality, consistency of findings, and plausibility. This form should also be documented in a traceable manner so that third parties can review the conclusions. <\/p>\n

Methodological building blocks: systematic search, selection, and assessment<\/h2>\n

Robust evidence synthesis starts with a clear research question (e.g., using PICO: Population, Intervention, Comparator, Outcome) and a systematic literature and database search. This is followed by predefined inclusion and exclusion criteria, a structured selection process, and extraction of relevant data points. Essential is the assessment of the internal and external validity of the included studies, for example through risk-of-bias considerations, sensitivity analyses, and discussion of generalisability to the target population. <\/p>\n

In regulated environments, it is also important that decisions made during the process are justified and documented with version control. This includes, for example, changes to search strategies, handling multiple publications from the same study, or dealing with missing data. Proper documentation supports audits and review processes and reduces the risk that the synthesis is assessed as selective or incomplete. <\/p>\n

Regulatory and HTA perspective (DE\/EU focus)<\/h2>\n

Authorities and assessment bodies expect the totality of evidence to be presented transparently, particularly when external data are used to substantiate assumptions. In clinical trials, principles of Good Clinical Practice are relevant, for example with regard to data integrity and transparent reporting. In the EU, it is also important that clinical data and their analysis are prepared consistently, auditable, and suitable for assessing benefits and risks under EU Regulation 536\/2014 (Clinical Trials Regulation). <\/p>\n

For medical devices, evidence synthesis can be part of the clinical evaluation when clinical data from literature and experience must be systematically consolidated. This requires a structured process that justifies the relevance, quality, and evidentiary value of the sources used and discloses remaining uncertainties. In practice, it is particularly important that statements on equivalence or clinical performance are based on a transparent evidence base and not solely on individual publications. <\/p>\n

Typical pitfalls and how to avoid them<\/h2>\n

A common pitfall is an overly broad or unclear research question, which makes the search unmanageable and leaves selection vulnerable to subjective decisions. Equally problematic is mixing non-comparable endpoints or populations, which can produce an apparently precise but substantively misleading overall conclusion. Selective inclusion of positive studies, lack of transparency around exclusions, or inadequate assessment of study quality can also undermine the credibility of the evidence synthesis. <\/p>\n

In practice, sponsors, CROs, and medical writing teams should define roles, responsibilities, and quality checks early on: Who formulates the research question? Who reviews the search strategy and data extraction? Which criteria apply for sensitivity analyses? Clear governance reduces rework later in dossier reviews and improves consistency across clinical reports, publications, and regulatory submissions. <\/p>\n

FAQ<\/strong><\/p>\n

Is Evidence Synthesis the same as a meta-analysis?<\/strong><\/p>\n

No. A meta-analysis is a quantitative method within evidence synthesis. Evidence synthesis also includes structured qualitative approaches, quality assessments, and transparent interpretation of the totality of evidence. <\/p>\n

When is a quantitative synthesis not appropriate?<\/strong><\/p>\n

If studies are highly heterogeneous (e.g., different endpoints, populations, or designs) or data are missing, purely statistical pooling can be misleading. In such cases, a structured qualitative synthesis is often more appropriate. <\/p>\n

What role does Evidence Synthesis play in regulatory submissions?<\/strong><\/p>\n

It supports the benefit\u2013risk argumentation, positioning within the therapeutic landscape, and the justification of assumptions or data gaps. A transparent process with clear documentation is essential. <\/p>\n

Regulatory References (Selection)<\/strong><\/p>\n

    \n
  • EU Regulation (EU) No 536\/2014 (Clinical Trials Regulation, CTR)<\/li>\n
  • Regulation (EU) 2017\/745 on medical devices (MDR)<\/li>\n
  • ICH E6(R3): Guideline for Good Clinical Practice<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    Evidence Synthesis (German: evidence synthesis) refers to methods used to consolidate existing scientific findings into a clear, transparent overall conclusion. In the development of pharmaceuticals and medical devices, evidence synthesis is used to assess efficacy, safety, and benefit\u2013risk aspects based on multiple sources rather than relying on a single study. The term covers both quantitative […]<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"glossary-cat":[],"class_list":["post-6724","glossary","type-glossary","status-publish","hentry"],"acf":[],"related_terms":"","external_url":"","internal_reference_id":"","_links":{"self":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6724","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary"}],"about":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/types\/glossary"}],"author":[{"embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/users\/10"}],"version-history":[{"count":0,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6724\/revisions"}],"wp:attachment":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/media?parent=6724"}],"wp:term":[{"taxonomy":"glossary-cat","embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary-cat?post=6724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}