How relationships between trial committees shape the quality of clinical trial oversight

What is it about?

The oversight of randomised controlled trials in the UK involves multiple stakeholder groups including Trial Steering Committees (TSCs), Trial Management Groups (TMGs), Data Monitoring Committees (DMCs), funders and sponsors, each with distinct roles and responsibilities. How these groups work together, and the quality of relationships between them, has direct implications for how effectively problems in trials are identified and managed. This ethnographic study, which follows a companion paper on TSC roles and functions, observed eight TSC and six TMG meetings from eight UK clinical trials facing challenges, and conducted 66 semi-structured interviews with 52 key informants. The study examined how relationships between oversight stakeholders affected the rigour and effectiveness of the oversight process, analysing data thematically and triangulating findings across multiple perspectives including those of trial managers, TSC chairs, sponsors and funders. The aim was to generate findings that could inform future revision of Good Clinical Practice (GCP) guidelines.

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Why is it important?

Good Clinical Practice guidelines govern how clinical trials must be conducted and overseen, yet the social and relational dynamics within trial oversight structures are rarely studied empirically. This ethnographic approach is unusual in the trials methodology literature and provides insights that regulatory and procedural guidance cannot: how the quality of working relationships, communication patterns, power dynamics and trust between committees shape whether oversight is genuinely protective of trial integrity and participant safety. The findings have direct practical relevance for trial sponsors, funders and clinical trials units designing governance structures, as well as for regulators reviewing GCP guidance. By studying trials under pressure, the research captures oversight functioning in conditions where its quality matters most, making the findings particularly actionable for improving the system.