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What is meant by the claim that something is "well documented"? How much confidence can we have in the results? The most commonly used method to grade the quality of the evidence is GRADE (Grading of Recommendations Assessment, Development and Evaluation). GRADE is used in systematic reviews, where the effects of different treatments or options are being compared, and in guidelines with recommendations about the competing options. GRADE makes a clear distinction between the quality of the evidence and the strength of recommendation. When grading the quality of the evidence with GRADE, the starting point is a well conducted systematic review of research on the question that is being assessed. Both randomized controlled trials and observational studies can be included in the evaluation. GRADE uses five criteria to judge for possible downgrading of the quality of the evidence: risk of bias, consistency, directness, precision and reporting bias. Observational studies may in addition be assessed for possible upgrading of the quality of evidence using the following three criteria: strong association, dose response relationship, and plausible confounding. All the judgments are noted and presented together with the results in tables. [ABSTRACT FROM AUTHOR]

Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered "homogeneous". But if the studies vary in their effect size and direction, the findings are "heterogeneous". The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies. [ABSTRACT FROM AUTHOR]