APPLICATION OF THE FRAMEWORK FOR EVALUATING COMPLEX INTERVENTIONS TO CLUSTER RANDOMIZED TRIALS FOR THE EVALUATION OF DISEASE MANAGEMENT PROGRAMS

Author(s)

Sara Marchisio, MD, PhD Student1, Massimiliano Panella, MD, Professor21University Politecnica delle Marche, Ancona, Italy; 2 Univerisity of Eastern Piedmont "A. Avogadro", Novara, NO, Italy

Trials of disease management programs pose several methodological challenges. Our objective is to assess the extent to which the various development steps of a cluster randomized trial to evaluate disease management are represented in the framework for the design and evaluation of complex interventions. The framework for evaluating complex interventions developed by Campbell and colleagues is composed by five phases: theoretical, identification of components of the intervention, definition of trial and intervention design, methodological issues for main trial, and promoting effective implementation. Using these phases the corresponding stages in the development of the cluster randomized trial to evaluate the effectiveness of disease management programs are identified and described. Synthesis of evidence needed to construct the program, survey and qualitative research used to define components of the program, a pilot study to assess the feasibility of delivering the care, methodological issues in the main trial including choice of design, allocation concealment, outcomes, sample size calculation and analysis are adequately represented using the stages of the framework for evaluating complex interventions. Even though is difficult to define precisely what exactly the “active ingredients” of a program of disease management and how they relate to each other, we think that the applied framework is a powerful resource for researchers planning a randomized clinical trial to evaluate the effectiveness of such programs.

Conference/Value in Health Info

2008-05, ISPOR 2008, Toronto, Ontario, Canada

Value in Health, Vol. 11, No. 3 (May/June 2008)

Code

PMC26

Topic

Methodological & Statistical Research

Topic Subcategory

Modeling and simulation

Disease

Multiple Diseases

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