Adaptive interventions treatment modelling and regimen optimization using Sequential Multiple Assignment Randomized Trials (SMART) and Q-learning

Nowadays, pharmacological practices are focused on a single best treatment to treat a disease which sounds impractical as the same treatment may not work the same way for every patient. Thus, there is a need of shift towards more patient-centric rather than disease-centric approach, in which personal characteristics of a patient or biomarkers are used to determine the tailored optimal treatment. The "one size fits all" concept is contradicted by research area of personalized medicine. The Sequential Multiple Assignment Randomized Trial (SMART) is a multi-stage trials to inform the development of dynamic treatment regimens (DTR's). In SMART, a subject is randomized through different stages of treatment where each stage corresponds to a treatment decision. These types of adaptive interventions are individualized and are repeatedly adjusted across time based on patient's individual clinical characteristics and ongoing performance. The reinforcement learning (Q-learning), a computational algorithm for optimization of treatment regimens to maximize desired clinical outcome is used in optimizing the sequence of treatments. This statistical model contains regression analysis for function approximation of data from clinical trials. The model will predict a series of regimens across time, depending on the biomarkers of a new participant for optimizing the weight management decision rules.