Circular instead of hierarchical: methodological principles for the evaluation of complex interventions

The hierarchical model is valid for limited questions of efficacy, for instance for regulatory purposes and newly devised products and pharmacological preparations. It is inadequate for the evaluation of complex interventions such as physiotherapy, surgery and complementary and alternative medicine (CAM). This has to do with the essential tension between internal validity (rigor and the removal of bias) and external validity (generalizability).Instead of an Evidence Hierarchy, we propose a Circular Model. This would imply a multiplicity of methods, using different designs, counterbalancing their individual strengths and weaknesses to arrive at pragmatic but equally rigorous evidence which would provide significant assistance in clinical and health systems innovation. Such evidence would better inform national health care technology assessment agencies and promote evidence based health reform.Evidence Based Medicine (EBM) has installed a canon of methods that are central to the methodological reasoning for evaluating medical interventions [1]. While originally developed for the evaluation of new pharmacological products [2,3], it is also applied to whole systems intervention approaches like nursing and psychotherapy, as well as the more complex interventions of Complementary and Alternative Medicine (CAM). EBM's main tool is the randomized controlled trial (RCT). Its essential principle is random assignment of a sufficiently large number of carefully selected patients to experimental and control groups, thereby evenly distributing known and unknown confounding variables. Changes in outcome can thus be attributed to the intervention(s).A hierarchy of methods has been described and utilized by health technology assessment (HTA) agencies, with case series, cohort studies with historical controls, non-randomized controlled studies being of lower value and having less methodological rigor than prospective RCTs. Only RCTs are considered for inclusion in many meta-analyses