Systematic Literature Review and Meta Analysis

Summary

This video provides a clear and practical guide to conducting systematic literature reviews and meta-analyses. It explains the three main steps: identifying relevant research based on predefined criteria, appraising study quality and evidence strength, and synthesizing findings through quantitative or qualitative methods. The video emphasizes the importance of clear research questions, using the PICO framework, and replicable methods. It also covers meta-analysis essentials, including forest plot interpretation and handling heterogeneity between studies. Viewers gain actionable insights into structuring literature searches, assessing study credibility, and effectively combining results to draw meaningful conclusions.

Takeaways

• 😀 A systematic literature review (SLR) involves three main steps: identifying relevant research, appraising study quality, and synthesizing findings.
• 😀 The PICO framework (Patient, Intervention, Comparison, Outcome) helps in clearly defining research questions for systematic reviews.
• 😀 A well-defined search strategy is essential to avoid being overwhelmed by too many papers and ensures replicability of the review process.
• 😀 Predefined inclusion and exclusion criteria (such as study type or publication date) are key to narrowing down the literature search effectively.
• 😀 Appraising study quality involves evaluating factors such as study size, type, methodology, and the risk of bias, such as 'p-hacking'.
• 😀 Meta-analysis is a statistical method for pooling results from multiple studies, typically displayed in a forest plot.
• 😀 Studies can only be combined in a meta-analysis if they are sufficiently similar, ensuring consistency across the research.
• 😀 Heterogeneity in studies (differences in population, intervention, outcome, methodology, or chance) can affect the validity of a meta-analysis.
• 😀 A forest plot in meta-analysis shows individual trial results, the pooled results, confidence intervals, and heterogeneity tests.
• 😀 Heterogeneity is assessed in meta-analysis using a p-value; a p-value less than 0.05 indicates significant variability across studies.
• 😀 A systematic literature review is a rigorous process that requires clearly stated methods to ensure transparency and replication of the research.

Q & A

What are the three main steps involved in conducting a systematic literature review?

The three main steps are: 1) Identifying relevant research based on a specific question and pre-specified eligibility criteria, 2) Appraising the quality and strength of the research studies, and 3) Synthesizing findings to draw conclusions, often using meta-analysis.

Why is it important for a systematic literature review to be replicable?

Replicability ensures that other researchers can follow the same methods to verify results or update the review as new evidence becomes available, maintaining transparency and reliability in research.

What does the PICO framework stand for and how is it used?

PICO stands for Patient/Problem/Population (P), Intervention (I), Comparison/Control (C), and Outcomes (O). It is used to clearly define the research question, which guides the literature search strategy.

What are some common inclusion and exclusion criteria used in literature searches?

Inclusion and exclusion criteria can include limiting the search to certain study types (e.g., randomized controlled trials), publication timeframes (e.g., last 10 years), and specific study populations or outcomes, with rationale provided beforehand.

How is the quality of a study assessed in a systematic review?

Quality is assessed based on factors like study type (RCTs are stronger than cohort studies, which are stronger than case-control studies), sample size, methodological rigor, and whether the study protocol was registered in advance.

What is p-hacking and why is registering a study protocol important?

P-hacking is when researchers consciously or unconsciously adjust methods to produce desired results. Registering a study protocol before conducting research helps prevent p-hacking and ensures research integrity.

What is meta-analysis and when is it appropriate to use?

Meta-analysis is a statistical method for quantitatively pooling results from multiple studies. It is appropriate when studies are sufficiently similar in terms of population, intervention, outcomes, and methodology.

What does a forest plot in a meta-analysis represent?

A forest plot shows individual study results (usually as odds ratios), their 95% confidence intervals, study weights (size of squares), and pooled results (diamond shape), along with tests for heterogeneity among studies.

What factors can contribute to heterogeneity between studies in a meta-analysis?

Heterogeneity can result from differences in populations, interventions, outcomes measured, methodological approaches, or random chance.

What are the differences between quantitative and narrative synthesis in systematic reviews?

Quantitative synthesis involves pooling numerical results using statistical methods like meta-analysis, while narrative synthesis is a qualitative approach that summarizes and interprets findings without statistical pooling. Reviews often combine both methods.

Why is using platforms like Nested Knowledge recommended for literature reviews?

Platforms like Nested Knowledge streamline the entire review process, including search, screening, tagging, extraction, synthesis, and manuscript writing, while facilitating team collaboration and creating a living, updateable document.

What should researchers do if new evidence becomes available after completing a systematic review?

Researchers should update the review using the same methods to incorporate new evidence, ensuring the conclusions remain current and relevant.