5 simple steps to conduct your first meta-analysis.

Discover how to choose a topic based on your field and craft a research question using Pico or AP approaches before moving to data extraction.

Choose a topic using the PICO framework—population, intervention, comparator, and outcome—guided by three questions and field-specific examples.

Apply tips for selecting meta-analysis topics using Pico and Pew research questions. Learn search strategies, ensure at least two similar papers, and avoid duplicating prior meta-analyses.

Explore how to identify relevant databases, craft a search strategy, and apply inclusion and exclusion criteria to filter papers for your first meta-analysis.

Learn how articles, journals, and databases differ and how databases let you search multiple journals at once, using PubMed and Cochrane Library for meta-analysis.

Learn to search the Cochrane Library using search manager, apply mesh terms for adolescent (ages 13–18), social media, and depression, and export the 162 studies to CSV for meta-analysis.

Learn to craft inclusion and exclusion criteria for meta-analyses by applying language, study type, participants, interventions, and outcomes to filter hundreds of papers.

Learn four-step filtration to narrow a large literature search to the final 10–20 studies for a meta-analysis, including uploading, deduplicating, title/abstract screening, and full-text review.

Learn how to download research papers, distinguish open access from paid articles, and use the DOI to access free full texts via PubMed and Sci-Hub.

Learn how to construct a Prisma flow diagram for a meta-analysis, detailing database searches, duplicate removal, screening, and inclusion of final studies.

Extract data from research papers for meta-analysis by reading papers, identifying key data, and recording them in Excel, while understanding the paper's basic structure.

Identify the basic structure of a research paper, including introduction, patient and method, results, discussion, conclusion, abstract, and references, and understand background, aim, and outcomes.

Identify basic data (sample size, age, sex, ASA or TNM) and outcome data (mortality, complications, pain) for intervention and control groups from results, tables, or figures, using Excel.

Learn how to identify and extract essential data for meta-analyses, including basic study data and outcome data, and use Excel to organize two-arm data per study.

learn meta-analysis statistics with review manager, including standard deviation, b value, and confidence interval, and practice data entry, heterogeneity assessment, and forest plot interpretation.

Learn to read forest plot curves in meta-analysis, including mortality outcomes, study weights, and risk ratios, and understand how pooled results and heterogeneity inform segmental versus extended colectomy.

Develop the introduction by detailing the background, incidence, and impact of the topic. Identify the gap of knowledge and state the aim for your meta-analysis.

Outline your meta-analysis workflow by detailing the search strategy, inclusion criteria, outcomes, and data extraction and statistics, including risk ratios and heterogeneity, with two authors verifying.

Explain how to write the results section, detailing included studies, data, and the Prisma flow diagram. Report outcomes like mortality, morbidity, and infection, including tables, figures, and statistical values.

Learn to write the discussion and conclusion for a meta-analysis in five parts: summarize results, discuss outcomes with prior studies, note strengths and limitations, and present a take home message.

Learn to write the reference section by citing sources with authors, title, journal, year and month, volume, pages, and doi, and choose the appropriate style (Vancouver, Chicago, MLA).

Learn to craft a concise meta-analysis abstract by detailing background and aim, method, results, and conclusion, including study counts and participant numbers.

Learn to publish your research by evaluating journals with questions on scope, indexing in PubMed and beyond, impact factor, fees, and publication time, then tailor abstracts, figures, tables, and references.

Identify and distinguish data types, including qualitative (categorical) and quantitative (numerical), with subtypes such as ordinal, nominal, continuous, discrete, and examples like mean, median, and standard deviation.

Explore mean, median, mode, range, and standard deviation, with practical examples and how these terms support reading research papers and conducting a meta-analysis.

understand how confidence intervals express a range for the mean and how sampling error influences estimates across repeated studies.

Explore what the p value means and how it relates to the null hypothesis. See how a 0.05 threshold guides interpretation using a weight-loss drug versus placebo example.

Identify heterogeneity as differences between study results in a meta-analysis. Use I^2 to gauge acceptability; if above 40, explore causes and switch from fixed-effect to random-effects model.

Explore probability, define odds and the odds ratio, and illustrate calculations with a box of balls to compare blue versus red outcomes and cross-group odds.

Interpret the risk ratio, a measure of relative risk between smokers and non-smokers, using incidence values of 0.6 and 0.2 to show a three times higher risk of lung cancer.