Benjamin Djulbegovic1, Iztok Hozo2, Shelly-Anne Li3, Marianne Razavi4, Adam Cuker5, Gordon Guyatt6. 1. Beckman Research Institute, Department of Computational & Quantitative Medicine, City of Hope, Duarte, CA; Division of Health Analytics, Duarte, CA; Evidence-based Medicine and Comparative Effectiveness Research, Duarte, CA. Electronic address: bdjulbegovic@coh.org. 2. Department of Mathematics, Indiana University, Gary, IN. 3. Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Canada. 4. Department of Supportive Medicine, Duarte, CA. 5. Department of Medicine and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. 6. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Abstract
OBJECTIVE: Many factors are postulated to affect guidelines developments. We set out to identify the key determinants. STUDY DESIGN AND SETTING: a) Web-based survey of 12 panels of 153 "voting" members who issued 2941 recommendations; b) qualitative analysis of 13 panels of 311 attendees (panel members, systematic review teams and observers). RESULTS: Compared with "no recommendations", when intervention's benefit outweigh harms (BH-balance), probability of issuing strong recommendations in favor of intervention was 0.22 (95%CI: 0.08 to 0.36) when certainty of evidence (CoE) was very low; 0.5 (95%CI:0.36 to 0.63) when low; 0.74 (95%CI 0.61 to 0.87) when moderate and 0.85 (95%CI:0.71 to 1.00) when high. No other postulated factor significantly affected recommendations. The findings are consistent with a J- curve model when recommendations are issued in favor but not against an intervention. Panelists often changed their judgments as a result of the meeting discussion (67% for CoE to 92% for balance between benefits and harms). The panels spent over 50% of their time debating CoE; the chairs and co-chairs dominated discussion. CONCLUSIONS: CoE and BH-balance are key determinants of recommendations in favor of an intervention. Chairs and co-chairs dominate discussion. Panelists often change their judgments as a result of panel deliberation.
OBJECTIVE: Many factors are postulated to affect guidelines developments. We set out to identify the key determinants. STUDY DESIGN AND SETTING: a) Web-based survey of 12 panels of 153 "voting" members who issued 2941 recommendations; b) qualitative analysis of 13 panels of 311 attendees (panel members, systematic review teams and observers). RESULTS: Compared with "no recommendations", when intervention's benefit outweigh harms (BH-balance), probability of issuing strong recommendations in favor of intervention was 0.22 (95%CI: 0.08 to 0.36) when certainty of evidence (CoE) was very low; 0.5 (95%CI:0.36 to 0.63) when low; 0.74 (95%CI 0.61 to 0.87) when moderate and 0.85 (95%CI:0.71 to 1.00) when high. No other postulated factor significantly affected recommendations. The findings are consistent with a J- curve model when recommendations are issued in favor but not against an intervention. Panelists often changed their judgments as a result of the meeting discussion (67% for CoE to 92% for balance between benefits and harms). The panels spent over 50% of their time debating CoE; the chairs and co-chairs dominated discussion. CONCLUSIONS: CoE and BH-balance are key determinants of recommendations in favor of an intervention. Chairs and co-chairs dominate discussion. Panelists often change their judgments as a result of panel deliberation.
Authors: Benjamin Djulbegovic; Muhammad Muneeb Ahmed; Iztok Hozo; Despina Koletsi; Lars Hemkens; Amy Price; Rachel Riera; Paulo Nadanovsky; Ana Paula Pires Dos Santos; Daniela Melo; Ranjan Pathak; Rafael Leite Pacheco; Luis Eduardo Fontes; Enderson Miranda; David Nunan Journal: J Eval Clin Pract Date: 2022-01-28 Impact factor: 2.336