This study examines tools and practices involved in a large scale and multi-sited clinical trial of a potential drug against obesity. Two tools are in focus: a clinical research protocol and a computer control system. The analysis is based on there being different ways in which the tools are localized in order for the work to flow smoothly and to produce reliable data. It does this through delineating different types of work performed: production tasks, classical managerial work, compliance work and the work of coordinating beliefs and goals. The study is based on interviews, observations and documentary analysis. Through describing these types of work and how it is organized, the study emphasizes the trial as being part of an industrial production process. Hence, the tools are used not only to produce reliable data, but also to manage the work of the tools’ users in order to enable a smooth production process. In line with such a description, the protocol and computer control system are seen as objects that discipline practice, something that also resonates in the way staff talk about their work. The dissertation shows how the tools, despite this, leave room for aspects of clinical trial work that are both rational/technical and experiential/contingent. The dissertation also shows that obesity is enacted in different ways in the practices performed in the trial. Making sense of these somewhat contradictory enactments requires work referred to as coordination of beliefs about what obesity is, as well as of the different goals of the trial. By such a focus on invisible work, the dissertation shows that those nurses, dieticians and doctors involved in the everyday follow-through of the trial have a strategic position in mediating between pharmaceutical companies and their potential market for the drug under study, namely the trial participants.

Randomised controlled trials (RCT) are considered the gold standard for assessing the efficacy and safety of medical interventions. However, RCTs face unique challenges when applied to medical technologies, such as issues related to timing of assessment, eligible population, acceptability, blinding, choice of comparator group, and consideration for learning curves. To address these challenges, this thesis explores the adaptation of the model-informed drug development (MIDD) approach to the field of medical technology, using a case study on transurethral microwave thermotherapy (TUMT). The research employs non-linear mixed- effects (NLME) modelling and D-optimal design to optimise study designs and improve the reliability and efficiency of clinical trials. The impact of different sampling times, sample sizes, and learning curves on effect size estimates is analysed. The results show that optimising sampling points and sizes significantly improves the precision and reliability of effect size estimates and describes how MIDD can be a useful tool for this purpose. The study also highlights the limitations of the TUMT study design, suggesting ways in which the model-based approach could offer more robust and reliable clinical evidence generation. This research highlights the potential of the MIDD approach to streamline the medical technology clinical development process, enhance the quality of evidence, and address its inherent complexities. Future work should expand on these findings by exploring more complex error models and additional study designs and its related aspects. / Randomiserade kontrollerade studier (RCT) anses vara standard för att bedöma effekt och säkerhet i kliniska interventionsstudier. RCT:er står dock inför unika utmaningar när de tillämpas på medicinteknik såsom utmaningar relaterade till tidpunkt för bedömning, rekrytering av lämpliga studiedeltagare, acceptans, blindning, val av jämförelsegrupp och hänsyn till inlärningskurvor. För att hantera dessa utmaningar undersöker denna avhandling anpassningen av modellinformerad läkemedelsutveckling (MIDD) till området medicinteknik, med hjälp av en fallstudie om transuretral mikrovågstermoterapi (TUMT). I arbetet tillämpas icke-linjär, hierarkisk (NLME) modellering och D-optimal design för att optimera studiedesigner och förbättra tillförlitligheten i kliniska prövningar. Effekten av olika observationstider, antal studiedeltagare och inlärningskurvor på estimeringen av effektstorlek analyseras. Resultaten visar att optimering av observationstidpunkter och studiestorlek avsevärt förbättrar precisionen och tillförlitligheten av den estimerade effektstorleken och visar på hur MIDD kan vara ett användbart verktyg för detta ändamål inom medicinteknisk utveckling. Studien belyser också begränsningarna i studiedesignen för fallstudien och föreslår hur en modellbaserad metod skulle kunna erbjuda mer robust och tillförlitlig generering av klinisk evidens. Denna forskning belyser potentialen hos MIDD-metoder för att effektivisera den medicintekniska kliniska utvecklingsprocessen, förbättra kvaliteten av evidens, och hantera dess inneboende komplexitet. Framtida arbete bör utvidga dessa resultat genom att utforska mer komplexa modeller, ytterligare studiedesigner, och relaterade aspekter.