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Closing the gap between 'mean effect size' and data desired by decision makers - exploring heterogeneity in complex interventions

Date and Location

Session: 

W1.04

Date: 

Friday 20 September 2013 - 10:30 - 12:00
Contact persons and facilitators

Contact person

Noah Ivers

Facilitators

Noah Ivers
Jeremy Grimshaw
Andrea Tricco
Thomas Trikalinos
Sharon Straus
Other contributors
First nameLast nameAffiliation and Country

First name

Issa

Last name

Dahabreh

Affiliation and Country

Brown University, USA

First name

Catherine

Last name

Yu

Affiliation and Country

Li Ka Shing Knowledge Institute, Canada

First name

John

Last name

Lavis

Affiliation and Country

McMaster University, Canada
Target audience

Target audience

Review authors and researchers focusing on complex interventions

Is your workshop restricted to a specific audience or open to all Colloquium participants?

Open

Level of knowledge required

Intermediate
Type of workshop

Type of workshop

Discussion
Abstract text

Abstract

Objectives To discuss the role of engaging decision makers to better understand their informational needs when reviewing complex interventions. To introduce novel statistical approaches for exploring heterogeneity of complex interventions. Description: Limitations in the reporting of primary studies of complex interventions and in the commonly used meta-analytical methods (including approaches for exploring heterogeneity) restrict the utility of existing systematic reviews for decision makers who wish to identify and optimize the design of new initiatives for their own context. Our recent systematic review of complex QI interventions for diabetes care, will serve as the foundation for these topics. We will introduce the role of and discuss methods for engaging stakeholders. Participants will briefly play the role of various stakeholders, including patients, providers, and policy makers, as an exercise in prioritizing key questions beyond ‘mean effect size’. We will demonstrate analytical approaches, including combinatorial meta-analysis techniques and hierarchical meta-regression that can be used to pursue answers of interest to decision makers. The pros and cons of agnostic/inductive and deductive approaches to explore heterogeneity will be examined, and the role of data enrichment through author surveys will be discussed. Broad application of these techniques in reviews of heterogeneous, complex interventions will be considered.