title: An introduction to systematic reviews and meta-analyses in health care.
abstract: Important issues in medical research are often examined in a number of studies, sometimes using different study designs. Data from such studies are often reviewed to provide a consensus in support or arguing against a particular hypothesis, such as the use of a new therapy/treatment or public health policy. However, it is important to recognise that while reviews can provide a rapid synthesis of the findings on a given issue, they may not represent a panacea of evidence. Evidence they provide is heavily dependent on the quality of studies that contribute to that review. Reviews of studies of good quality will provide stronger evidence for the research question under investigation, compared to reviews of studies with weaker methodology. Reviewers who 'cherry pick' studies to be included may bias findings towards a preconceived hypothesis. Hence, it is important when reviewing evidence that all studies on a given topic are identified and included where possible, i.e. the review is systematic, reproducible and representative of the totality of evidence; a so called 'systematic review'. This article aims to familiarise those in the ophthalmic sector with methods/guidelines used to improve the quality of studies and systematic reviews. It will also outline how numerical data obtained from a systematic review can be combined using statistical methods called 'meta-analysis'. By combining numerical estimates from different studies we can be more precise about the estimate for an effect or outcome of interest.
authors.affiliation: Division of Population Health Sciences and Education, St George's University of London, London, UK. arudnick@sgul.ac.uk
authors.forename: Alicja R
authors.initials: AR
authors.lastname: Rudnicka
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Owen
mesh_terms: D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods; D006801:Humans; D015201:Meta-Analysis as Topic; D012107:Research Design; D012196:Review Literature as Topic