TY - JOUR
T1 - GRADE guidelines: 7. Rating the quality of evidence - Inconsistency
AU - Guyatt, Gordon H.
AU - Oxman, Andrew D.
AU - Kunz, Regina
AU - Woodcock, James
AU - Brozek, Jan
AU - Helfand, Mark
AU - Alonso-Coello, Pablo
AU - Glasziou, Paul
AU - Jaeschke, Roman
AU - Akl, Elie A.
AU - Norris, Susan
AU - Vist, Gunn
AU - Dahm, Philipp
AU - Shukla, Vijay K.
AU - Higgins, Julian
AU - Falck-Ytter, Yngve
AU - Schünemann, Holger J.
PY - 2011/12
Y1 - 2011/12
N2 - This article deals with inconsistency of relative (rather than absolute) treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I 2. To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large vs. small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low P-values; and have a biological rationale.
AB - This article deals with inconsistency of relative (rather than absolute) treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I 2. To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large vs. small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low P-values; and have a biological rationale.
UR - http://www.scopus.com/inward/record.url?scp=80054981259&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2011.03.017
DO - 10.1016/j.jclinepi.2011.03.017
M3 - Article
C2 - 21803546
AN - SCOPUS:80054981259
SN - 0895-4356
VL - 64
SP - 1294
EP - 1302
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
IS - 12
ER -