Meta-analysis

• "A systematic review answers a defined research question by collecting and summarising all empirical evidence that fits pre-specified eligibility criteria."
• "A meta-analysis is the use of statistical methods to summarise the results of these studies."

Reference

• Decide what question or problem you're going to attack
• Come up with a hypothesis
• Do an exhaustive literature review to find everyone else who's attempted to answer the same question
• Decide whether you can use these studies by comparing them to your predefined inclusion criteria (sample size/blinding/randomisation)
• Preferably get multiple people to agree on which studies are to be included
• Do some stats and figure out what the effect size of each study is - bigger study = bigger effect size
• Summarise and present the results using pretty graphs and something called a forest plot.

• It's a reliably objective and quantitative appraisal of the available evidence
• Increased power - small individual studies often lack sufficient power to draw any meaningful conclusions, so combining multiple studies can get around this issue - this means a meta-analysis has a lower risk of a false negative result
• Increased precision - you getter a better idea of actual effect sizes as a result of a much bigger combined sample size
• Summary of the literature - the process of meta-analysis necessitates reviewing as much of the literature as possible, which brings multiple separate avenues of research together and can help identify other areas for exploration
• Patterns - even if you can't conclude anything meaningful, meta-analysis can often identify trends or patterns that might stimulate interest in further research
• Can help guide future research

• The studies are often all different in their inclusion criteria, what they're actually measuring as an outcome and their methodology, which can make comparing them rather hard work
• You might not include all the relevant studies, either through not searching correctly, inappropriately discounting them or not seeing them at all because they were published in obscure journals
• Publication bias means that studies with an interesting outcome are more likely to get published, so when you're doing a meta-analysis you often don't see the other studies where nothing interesting was found
• Bad studies = bad meta-analysis
• Clinical relevance to the individual patient is not guaranteed, even if you have strong statistical results
• You will have to make some assumptions along the way during your meta-analytic journey, and these might introduce error to the results
• A positive meta-analysis isn't enough to drive a change in practice, and usually still needs confirmation with a large scale randomised controlled trial

• Bigger sample size
• Bigger effect size (box further from vertical line)
• Greater precision (narrower box)
• Smaller confidence interval