Insights

Evidence synthesis for healthcare decision-making

by Kristen Markus

What is evidence synthesis?

As the pharmaceutical market rapidly evolves to keep pace with patient needs and emerging research, healthcare decision makers must continuously re-evaluate the evidence base that informs their thinking. But how can they take into account all the relevant data? How can they decide what new information should replace or merely extend existing knowledge? Answering these questions requires effective evidence synthesis.

Evidence synthesis for decision making in healthcare is the process of collecting and integrating all relevant scientific information available in order to draw well-informed conclusions. Conclusions derived from multiple studies are generally more clinically meaningful than those from a single study. Combining data from multiple studies in a careful way can reduce the influence of bias, methodological flaws or uncertainty due to poor statistical power that can severely compromise single studies.  

In this way, synthesising evidence increases the overall certainty of results and ensures that clinicians and regulatory agencies can confidently proceed with evidence-based practices to provide patients with the highest standard of care.

How is evidence synthesised?

Researchers in health economics and outcomes address the research questions of drug and device manufacturers by assembling exhaustive evidence bases for assessing a new product’s potential impact in the healthcare market. Researchers work in close partnership with manufacturers to define an appropriate problem or question, which is used to formulate queries to identify relevant publications and other information sources. The queries should be appropriately focused on the appropriate population, intervention, and comparators. Queries should also be sufficiently sophisticated to generate an evidence base that can shed light on various healthcare scenarios. Thus, patient and therapy details may also need to be included, such as disease progression, drug dosing, and treatment history.

Queries are used to systematically review the literature to support decision making in healthcare. In contrast to a narrative literature review, a systematic review attempts to identify all relevant evidence based on a rigorous, well-defined methodology (see below). A statistical analysis, usually meta-analysis, is performed to synthesise the results of the studies identified in the systematic review. If certain interventions have not been directly compared to each other in a head-to-head trial, a network meta-analysis can be performed to generate pairs of direct and indirect comparisons that connect up all interventions into a single network. The outcome of (network) meta-analysis is an estimate of how well each treatment performs in comparison with all the others. On this basis, treatments can be ranked from best to worst, and such rankings can be extended with economic modelling to allow drug and device manufacturers to establish a niche for their product on the healthcare market.

Evidence synthesis: Comparison of evidentiary strength obtained through a narrative literature review, systematic review or via a meta-analysis.

Importance of evidence synthesis

Pharmaceutical and health technology manufacturers must continually develop new therapies and refine existing ones in order to meet the ever-changing needs of the healthcare system. Through evidence synthesis, decisions can be made based on all available facts, limiting the pernicious influence of potential bias or confounding that can compromise the quality of patient care. Sourcing all relevant evidence allows researchers to identify gaps in the literature where future research may be required, and it allows practitioners and policy makers to formulate frameworks and guidelines when the evidence base is adequate. Regulatory agencies rely on systematic reviews to make decisions that have taken into account all available evidence. 

Evidence synthesis and Symmetron

At Symmetron, our experience and success give us the confidence to delivering our clients with the conclusive evidence they need to bring their product to the market. Our team bridges the gap in existing evidence to give patient’s access to novel, effective treatments. Join us in continuing to drive healthcare forward by contacting us here.

A

Average cost

C

Cost-benefit analysis

Cost-effectiveness analysis

Cost-effectiveness threshold

H

Healthcare system

Health economics

Health outcome

Health state

Health technology assessment

I

Incremental cost

Incremental cost-effectiveness ratio

 

M

Marginal cost

O

Opportunity cost

Q

Quality-adjusted life years

Quality of life and health-related quality of life

T

Total cost

U

Utility

Kristen Markus was a Health Outcomes Associate at Symmetron.

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