Ashwell Gunn and Gibson Ob Revs.pdf. 12.2.1 The GRADE approach. The Cochrane Collaboration open learning material. Handbook for Systematic Reviews of Interventions. Study Design 101 - Helpful formulas. Sensitivity and Specificity Test result b = false negative c = false positive To estimate sensitivity The number of positive test results for the presence of an outcome (a) divided by the total presence of an outcome (a+b) Sensitivity = a / (a+b) To estimate specificity Number of negative test results for the absence of an outcome (d) divided by the total absences of an outcome (c + d) Specificity = d / (c+d) False Positive and False Negative rates Test Result To calculate rate of false positives The number of false positive test results for an outcome (c) divided by the total number of absences of an outcome (c+d) Rate of false positives = c / (c+d) To calculate the rate of false negatives The number of false negative test results for an outcome (b) divided by the total number of presences of an outcome (a+b) Rate of false negatives = b / (a+b) Positive Predictive Value and Negative Predictive Value To estimate positive predictive value Positive predictive value = a / (a+c) Relative Risk Outcome Cohort.
Meta-analysis in medical research. Meta-an. Meta-analysis: Principles and procedures. Matthias Egger, reader in social medicine and epidemiology (egger@bristol.ac.uk)a, George Davey Smith, professor of clinical epidemiologya, Author Affiliations Correspondence to: Dr Eggerm Introduction Meta-analysis is a statistical procedure that integrates the results of several independent studies considered to be “combinable.”1 Well conducted meta-analyses allow a more objective appraisal of the evidence than traditional narrative reviews, provide a more precise estimate of a treatment effect, and may explain heterogeneity between the results of individual studies.2 Ill conducted meta-analyses, on the other hand, may be biased owing to exclusion of relevant studies or inclusion of inadequate studies.3 Misleading analyses can generally be avoided if a few basic principles are observed.
Observational study of evidence Meta-analysis should be viewed as an observational study of the evidence. The Cochrane Collaboration open learning material. Meta-analysis is the use of statistical methods to combine results of individual studies. This allows us to make the best use of all the information we have gathered in our systematic review by increasing the power of the analysis. By statistically combining the results of similar studies we can improve the precision of our estimates of treatment effect, and assess whether treatment effects are similar in similar situations. The decision about whether or not the results of individual studies are similar enough to be combined in a meta-analysis is essential to the validity of the result, and will be covered in the next module on heterogeneity.
In this module we will look at the process of combining studies and outline the various methods available. There are many approaches to meta-analysis. We have discussed already that meta-analysis is not simply a matter of adding up numbers of participants across studies (although unfortunately some non-Cochrane reviews do this).