• Gordon H Guyatt, Andrew D Oxman, Regina Kunz, James Woodcock, Jan Brozek, Mark Helfand, Pablo Alonso-Coello, Yngve Falck-Ytter, Roman Jaeschke, Gunn Vist, Elie A Akl, Piet N Post, Susan Norris, Joerg Meerpohl, Vijay K Shukla, Mona Nasser, Holger J Schünemann, and GRADE Working Group.
• Department of Clinical Epidemiology and Biostatistics, McMaster University, 1200 Main Street, West Hamilton, Ontario L8N 3Z5, Canada. guyatt@mcmaster.ca
• J Clin Epidemiol. 2011 Dec 1; 64 (12): 1303-10.

AbstractDirect evidence comes from research that directly compares the interventions in which we are interested when applied to the populations in which we are interested and measures outcomes important to patients. Evidence can be indirect in one of four ways. First, patients may differ from those of interest (the term applicability is often used for this form of indirectness). Secondly, the intervention tested may differ from the intervention of interest. Decisions regarding indirectness of patients and interventions depend on an understanding of whether biological or social factors are sufficiently different that one might expect substantial differences in the magnitude of effect. Thirdly, outcomes may differ from those of primary interest-for instance, surrogate outcomes that are not themselves important, but measured in the presumption that changes in the surrogate reflect changes in an outcome important to patients. A fourth type of indirectness, conceptually different from the first three, occurs when clinicians must choose between interventions that have not been tested in head-to-head comparisons. Making comparisons between treatments under these circumstances requires specific statistical methods and will be rated down in quality one or two levels depending on the extent of differences between the patient populations, co-interventions, measurements of the outcome, and the methods of the trials of the candidate interventions.Copyright © 2011 Elsevier Inc. All rights reserved.

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