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From Standardization to Adaptation: Clinical Trials and the Moral Economy of Anticipation
Authors:Catherine M. Montgomery
Affiliation:Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands
Abstract:Hailed as the gold standard, the randomized controlled trial (RCT) occupies a hegemonic position at the top of evidence-based medicine’s hierarchy of knowledge. It is testament to the methodology’s capacity for standardization that it can so readily be spoken of in the singular: the RCT. Under what conditions, then, is it possible to speak of change in the gold standard? Since the 1950s, alternative versions of the RCT have been advocated for under the banner of ‘adaptive design’. Adaptive designs allow investigators to make pre-planned changes to a trial on the basis of accruing information while the experiment is ongoing. Initially a niche topic of methodological debate among biostatisticians, the approach is becoming widespread in mainstream drug development. A genealogical analysis exposes the discursive moves used to justify and popularize adaptation, from a focus on patient well-being and the greater good in the 1960s and 1970s, to efficiency and virtualism in the 1990s and 2000s. Changing discourses of time and patienthood have facilitated a move away from standardization as the singular logic of trials towards an appreciation of flexibility, undergirded by probabilistic methodologies. Adams et al.’s [(2009). Anticipation: Technoscience, life, affect, temporality, Subjectivity, 28, pp. 246–265] conceptual framework of anticipation illuminates this evolving moral economy of medical research, in which modes of knowledge production which claim to know the future are supplanting the traditional certainties of fixed and standardized experimental designs. Predictable uncertainty is the currency of this emerging economy, which capitalizes on computer simulation and ever more sophisticated tools of prediction to leverage credibility.
Keywords:clinical trials  adaptive design  time  patients  statistics  anticipation
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