A multi stage clinical trial method for personalised medical care has been developed by the researchers from Indian Institute of Technology Guwahati (IITG). The method is devised based on the response of the patients during the trails, ensuring a custom made healthcare solution is ensured.
The research, conducted in collaboration with Duke-NUS Medical School, the National University of Singapore, Singapore, and the University of Michigan, USA focuses on Dynamic Treatment Regimes (DTRs) designed through Sequential Multiple Assignment Randomised Trials (SMARTs), in an attempt to devise strategies for patients with varying responses to therapies over time.
DTR, a modern decision rules, recommends switching drugs or combining therapies based on the outcome of the treatment on a patient. Instead of a standardised one suggestion/treatment for all, the DTR recommends changing medications based on the response of the patients.
If the blood sugar level of a patient is not reducing as expected, the DTR suggests changes in drugs based on the intermediate outcomes of the present medications. In this way every patient would be provided with customized personal medical care.
SMART methodology uses multi-stage clinical trials to develop effective DTRs which allows researchers to evaluate different treatment sequences and match patients with suitable therapies. SMART differs from typical trials by incorporating multiple treatment phases, adjusting patient assignments based on how they respond to initial treatments.
Speaking about the research Dr. Palash Ghosh, Assistant Professor, Dept. Of Mathematics, IITG, said, “Adaptive designs like this would encourage more patient participation in clinical trials like SMART. When patients see they are receiving treatments tailored to their needs, they are more likely to stay engaged. This approach also has vast potential for public health interventions, such as tailoring substance abuse recovery plans to individual needs as well as in other chronic diseases.”
Traditional SMART trials allocate patients equally across treatment options, in spite of early evidence showing less effectiveness of some treatments. To address this limitation, the researchers have developed a new adaptive randomization approach. This method uses real-time trial data to adjust how patients are allocated to different treatments, to ensure better results during the trial.
This innovative approach maintains scientific validity while ensuring more patients receive the most promising treatments. By evaluating both immediate and long-term results, the method enhances overall treatment success rates and patient outcomes.
