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Optimising the design of invasive placebo interventions in randomised controlled trials

Research output: Contribution to journalArticle

Original languageEnglish
JournalBritish Journal of Surgery
DateAccepted/In press - 13 Dec 2019


Placebo-controlled trials play an important role in the evaluation of healthcare interventions. However, they can be challenging to design and deliver for invasive interventions, including surgery. In-depth understanding of the component parts of the treatment intervention (to ascertain what should, and should not, be delivered as part of the placebo) is needed. Assessment of risk to patients and strategies to ensure the placebo effectively mimics the treatment are also required. To date no guidance exists for the design of invasive placebo interventions.

This study aimed to develop a framework to optimise the design and delivery of invasive placebo interventions in RCTs.

A preliminary framework was developed using published literature to i) expand the scope of an existing typology, which facilitates the deconstruction of invasive interventions, and ii) identify placebo optimisation strategies. The framework was refined after consultation with key stakeholders in surgical trials, consensus methodology and medical ethics.

The resultant DITTO framework consists of five stages: Stage 1 - Deconstruct treatment intervention into constituent components and co-interventions; Stage 2 - Identify critical surgical element(s); Stage 3 - Take out the critical element(s); Stage 4 - Think risk, feasibility, and role of placebo in trial when considering remaining components; and Stage 5 - Optimise placebo to ensure effective blinding of patients and trial personnel.

DITTO considers invasive placebo composition systematically, accounting for risk, feasibility and placebo optimisation. Use of the framework can support the design of high-quality RCTs, which are needed to underpin delivery of healthcare interventions.

    Research areas

  • placebo, invasive procedures, surgery, methodology

    Structured keywords

  • Bristol Population Health Science Institute



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