Randomized controlled trials (RCTs) of complex interventions are conducted in a context-specific environment. Principal trial reports, with a focus on main results, are unable to document adequately the context for an intervention, for example, word limits. Important context may be included in “sibling studies” (that is, studies conducted alongside the main trial, for example, process evaluations, and qualitative studies (1). This presentation explores (i) to what extent is it possible to use a systematic and parsimonious method to identify sibling studies and (ii) what is the potential value of yield from these studies?

The systematic CLUSTER approach (2) to follow up of index studies (Citations, Lead authors, Unpublished materials, Scholar, Theories, Early examples Related projects) has demonstrated the value of retrieved items in qualitative terms. However, the CLUSTER approach is painstaking and laborious and may be prohibitive within a time-tight Health Technology Assessment (HTA). A streamlined CLUSTER approach, using freely available Publish or Perish Software integrated with Google Scholar and Microsoft Excel, offers an economical way of building up “clusters” of study reports. A case study of a UK National Institute for Health Research (NIHR)-funded HTA on the management of medically unexplained symptoms in primary care, utilizing quantitative and qualitative research studies, is used to examine the practical application of the approach.

Systematic comparison of yield from sifting with yield from the Publish or Perish software reveals (i) major trials for which corresponding qualitative studies were not previously identified, (ii) qualitative studies identified independently from, and potentially unlinked to, associated trials, (iii) associated trial reports (for example, protocols, feasibility studies, etc), economic evaluations and systematic reviews, and (iv) commentaries and correspondence; all with the potential to enhance understanding of trial context.

The potential of the Publish or Perish-enabled CLUSTER approach to identify trials or qualitative studies, through “joining up” and mapping of clusters, potentially missed from separate quantitative/qualitative sift processes, means that it should be considered for any HTA that seeks to integrate quantitative and qualitative studies.