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IDEAL and Research Waste in the Study of Complex Interventions

Peter McCulloch, Fei Shan, Nicole Bilbro & Allison Hirst

A seminar reporting a rapid systematic review and a sampling review and analysis of current literature with a discussion of how IDEAL should reduce research waste and an attempt to evaluate it’s potential impact.

  1. High level comparative literature overview of research quality, quantity and sources of bias in current research on surgery, therapeutic devices and pharma
  2. Types of Research Waste (REWARD classification) and typical faults of research on complex interventions
    1. Asking the wrong question: focusing on outcomes before demonstrating stability
    2. Poor design: Failure to achieve consensus over intervention definition, delivery fidelity, indications; failure to conduct qualitative research on attitudes and values
    3. Failure to publish: Failed trials due to poor preparatory studies
    4. Poor reporting: Failure to use standard definitions, report modifications to intervention or indication, study learning curves or report delivery fidelity
  3. Mechanisms by which IDEAL should reduce research waste
    1. Increasing transparency and clarity (d)
    2. Reducing repetition due to misunderstanding or ignorance (b,d)
    3. Increasing speed of recognition of intervention stability (a,b)
    4. Avoidance of waste due to invalid studies arising from quality and learning effects (b,c)
    5. Increased speed and probability of consensus around an RCT (b,c)
    6. Reduced waste from RCT failure due to lack of consensus or equipoise (b,c)
  4. Theoretical quantification of overall benefit from using IDEAL – sampling study of current literature

2017 Highlights