Clinical studies typically provide evaluation of the mean effects of a specific treatment. However, from a clinical perspective, we should aim for more than that; we should not just want to know how on average the specific treatment works, but rather to predict how each individual patient would react to the treatment. In this context, the future belongs to precision or personalized medicine, where treatment is tailored to each subgroup or even to individual patients.
Recently, Kappelmann et al. published an article “Adapting the randomised controlled trial (RCT) for precision medicine: introducing the nested-precision RCT (npRCT).” In this study, the authors emphasized that RCTs are not appropriate trial design for personalized medicine. They suggested an adaptation of RCT design, named nested-precision RCT (npRCT), to facilitate development of precision medicine algorithms that aim to optimize treatment allocation for individual patients. They wrote: “The npRCT combines a traditional RCT (intervention A versus B) with a precision RCT (stratified versus randomized allocation to A or B).” They also emphasize that “as both the traditional and the precision RCT include participants randomized to interventions of interest, data from these participants can be jointly analyzed to determine the comparative effectiveness of intervention A versus B, thus increasing statistical power.” To show empirical superiority over placebo or current gold standard treatments, precision algorithms should exhibit empirical superiority over random allocation to interventions. The authors also quantified savings of the npRCT compared with two independent RCTs by highlighting sample size requirements and by introducing an open-source power calculation app.
In conclusion, the npRCT may be a novel precision medicine trial design strategy to efficiently combine traditional and precision RCTs, and this and other adaptations to the standard RCTs should be considered if we want to go in the direction of personalized medicine.