Evidence Synthesis & Health Technology Assessment
Research growth and the related exponential rate of accumulation of publications have escalated the need for effective and efficient methods to synthesize the evidence base. The ultimate goal of evidence synthesis should be to produce timely and transparent knowledge that meets the needs of decision-makers in health and healthcare. Decision-makers include patients, providers (ie, healthcare providers who recommend drugs and other technologies for prevention, diagnosis, prognosis, treatment, and monitoring of disease), policymakers (governments and health administrators who decide which drugs, technologies, and health programs will be funded), and researchers (who need to understand the evidence base, and its gaps, in order to design research to address remaining gaps).
Systematic review and meta-analysis (SR/MA) are recognized methodologies for synthesizing the evidence base. Accepted methodologies for synthesizing evidence for patient-level or population-level benefits and risks have been significantly expanded to include additional considerations such as assessment of economic impacts, equity considerations, socio-legal-ethical implications, and other contextual issues through a combination of qualitative and quantitative methods.
Health technology assessment (HTA) is a closely related field that combines evidence synthesis with policy and contextual implications for ‘technologies’ broadly defined as drugs, devices, medical/surgical procedures, and programs of care, or any combination of these. HTA has been formally defined as “systematic evaluation of the properties and effects of a health technology addressing the direct and intended effects of this technology, as well as its indirect and unintended consequences, ad aimed mainly at informing decision making regarding health technologies. HTA is conducted by interdisciplinary groups that use explicit analytical frameworks drawing on a variety of methods.” (www.inahta.org)
A number of innovative methodologies have been proposed to address statistical and other non-statistical quandaries of evidence synthesis and HTA. For example, pairwise meta-analysis has expanded to include indirect comparisons and network meta-analysis; estimation and interpretation of heterogeneity and meta-regression have expanded to include issues of contextual analysis; improvements in conveying results through data visualizations and decision-friendly summaries. Furthermore, a number of innovations have been proposed to improve the efficiency of systematic review, meta-analysis, and HTA through artificial intelligence and machine learning to expedite the time-intensive steps of identifying and filtering relevant evidence, assessing risk of bias, and extracting data.
The members of the Evidence Synthesis and Health Technology Assessment Cluster in the Department of Epidemiology & Biostatistics will support advances in evidence synthesis and health technology assessment for the purpose of improving health and health care outcomes both locally and globally.
- Skills and capacity building to support effective and efficient systematic reviews, meta-analyses, and HTAs to inform clinically-relevant and policy-relevant questions for local or global contexts.
- Development and exploration of innovative methods to improve efficiency of identifying evidence, filtering evidence, assessing risk of bias, extracting information, and synthesizing results.
- Development and exploration of innovative approaches to contextualizing the evidence and communicating the implications of the results of evidence syntheses and HTAs.
- Development of methods to improve decision-maker relevance and real-world impact.