New Delhi, Feb 3 – A groundbreaking multi-stage clinical trial methodology has been developed by researchers from the Indian Institute of Technology (IIT) Guwahati, the National University of Singapore, and the University of Michigan. This innovative approach aims to transform personalized medical care by adapting treatment plans in real-time according to each patient’s unique responses during clinical trials.

The research findings, co-authored by Palash Ghosh and Rik Ghosh from IIT Guwahati, Bibhas Chakraborty from Duke-NUS Medical School, National University of Singapore, and Inbal Nahum-Shani and Megan E Patrick from the University of Michigan, USA, have been published in the prestigious journal Biometrics.

At the core of this research are Dynamic Treatment Regimes (DTRs), which are developed through Sequential Multiple Assignment Randomised Trials (SMARTs). These frameworks address the significant challenge of optimizing treatment strategies for patients whose responses to therapies may vary over time.

DTRs utilize advanced decision rules that allow for dynamic adjustments in treatment as a patient’s condition changes. For instance, if a diabetes patient does not respond favorably to an initial medication, the DTR can suggest switching to a different drug or combining therapies. By factoring in intermediate outcomes, such as fluctuations in blood sugar levels, DTRs move away from a one-size-fits-all approach, providing care that is specifically tailored to individual progress and requirements.

“Multi-stage clinical trials are crucial for developing effective DTRs, and the SMART methodology allows researchers to evaluate various treatment sequences to determine the most suitable option for each patient,” stated Palash Ghosh, Assistant Professor in the Department of Mathematics at IIT Guwahati. “Unlike traditional trials, SMART involves multiple treatment stages, where patients are reassigned based on their responses to previous interventions.”

Ghosh further explained that traditional SMART trials often assign patients to treatment arms in equal numbers, even when some treatments are less effective, based on interim data. This can lead to unnecessary treatment failures. The new adaptive randomization method developed by the team dynamically allocates patients to treatment arms based on real-time trial data, optimizing patient allocation ratios in favor of the most effective treatment sequences at any given moment during the trial.

This innovative approach aims to ensure that a greater number of patients receive effective treatments while upholding scientific rigor. “By concentrating on both short-term and long-term outcomes, this method will enhance the overall treatment process, minimizing failures and improving patient care. Adaptive designs like this are likely to encourage increased patient participation in clinical trials such as SMART. When patients recognize that they are receiving treatments tailored to their specific needs, they are more inclined to remain engaged,” Ghosh added.

The potential applications of this research extend to public health interventions, including the customization of substance abuse recovery plans and management strategies for other chronic diseases. The research team is currently collaborating with Indian medical institutions to implement SMART trials aimed at effectively managing mental health issues using traditional Indian medicines.