GRADE guidelines: 4. Rating the quality of evidence--study limitations (risk of bias).

TitleGRADE guidelines: 4. Rating the quality of evidence--study limitations (risk of bias).
Publication TypeJournal Article
Year of Publication2011
AuthorsGuyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P, Montori V, Akl EA, Djulbegovic B, Falck-Ytter Y, Norris SL, Williams JW, Atkins D, Meerpohl J, Sch├╝nemann HJ
JournalJournal of clinical epidemiology
Volume64
Issue4
Pagination407-15
Date Published2011 Apr
ISSN1878-5921
KeywordsEvidence-Based Practice; Female; Humans; Male; Practice Guidelines as Topic; Publication Bias; Quality Assurance, Health Care; Randomized Controlled Trials as Topic; Risk Assessment
Abstract

In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if most of the relevant evidence comes from studies that suffer from a high risk of bias. Well-established limitations of randomized trials include failure to conceal allocation, failure to blind, loss to follow-up, and failure to appropriately consider the intention-to-treat principle. More recently recognized limitations include stopping early for apparent benefit and selective reporting of outcomes according to the results. Key limitations of observational studies include use of inappropriate controls and failure to adequately adjust for prognostic imbalance. Risk of bias may vary across outcomes (e.g., loss to follow-up may be far less for all-cause mortality than for quality of life), a consideration that many systematic reviews ignore. In deciding whether to rate down for risk of bias--whether for randomized trials or observational studies--authors should not take an approach that averages across studies. Rather, for any individual outcome, when there are some studies with a high risk, and some with a low risk of bias, they should consider including only the studies with a lower risk of bias.

DOI10.1016/j.jclinepi.2010.07.017
Alternate JournalJ Clin Epidemiol