The Centre for Evidence-Based Medicine in Oxford (UK) provides tools to develop, teach and promote evidence-based health care. Has useful tools and downloads for the critical appraisal of medical evidence. Example appraisal sheets are provided together with several helpful examples.
The Critical Appraisal Skills Programme (CASP) helps people to find and interpret the best available evidence from health research.
This scale was developed to assess the quality of nonrandomised studies with its design, content and ease of use directed to the task of incorporating the quality assessments in the interpretation of meta-analytic results.
AMSTAR2 is an updated version of AMSTAR. It is a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both.
RoB 2 is the revised (version 2) Cochrane risk-of-bias tool for randomized trials. It is the recommended tool to assess the risk of bias in randomized trials included in Cochrane Reviews.
The ROBINS-I tool is based on the Cochrane RoB tool for randomized trials. It is used to evaluate the risk of bias (RoB) in the results of non-randomized studies of interventions (NRSI) that compare the health effects of two or more interventions.
A checklist that was designed to provide a quality appraisal tool for quantitative, qualitative and mixed methods studies included in systematic mixed studies reviews
Developed by the JBI and collaborators to assess the methodological quality of different study designs for systematic reviews..
Useful calculators for Therapy, Harm, Diagnostic Studies and measuring Kappa- from JAMAevidence ( login required)
Evidence Based Medicine Calculators The CEBM Statistics Calculator was created for your own personal use and testing purposes. It is to be used as a guide only. Medical decisions should NOT be based solely on the results of this program. Although this program has been tested thoroughly, the accuracy of the information cannot be guaranteed.
Diagnostic Test Calculator This calculator can determine diagnostic test characteristics (sensitivity, specificity, likelihood ratios) and/or determine the post-test probability of disease given given the pre-test probability and test characteristics. Given sample sizes, confidence intervals are also computed.