Guwahati: Researchers from the Indian Institute of Technology (IIT) Guwahati, the National University of Singapore, and the University of Michigan have developed a novel multi-stage clinical trial method designed to revolutionize personalized medical care.
This innovative approach adapts treatment plans in real-time, based on each patient’s unique responses during the trial, leading to more tailored and effective healthcare solutions.
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The research, published in the journal Biometrics, was 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.
The study focuses on Dynamic Treatment Regimes (DTRs) developed through Sequential Multiple Assignment Randomized Trials (SMARTs).
These frameworks address the challenge of optimizing treatment strategies—sequences of treatments—for patients who respond differently to therapies over time.
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DTRs are advanced decision rules that dynamically adapt treatments as a patient’s condition evolves. For example, if a diabetes patient doesn’t respond well to an initial medication, the DTR might recommend switching drugs or combining therapies.
By incorporating intermediate outcomes, such as changes in blood sugar levels, DTRs move beyond the one-size-fits-all model, tailoring care to individual progress and needs.
“Multi-stage clinical trials are essential for developing effective DTRs, and SMART methodology enables researchers to test various treatment sequences to find the best fit for each patient,” said Palash Ghosh, Assistant Professor, Department of Mathematics, IIT Guwahati.
“Unlike traditional trials, SMART involves multiple stages of treatment, where patients are reassigned based on their responses to earlier interventions,” Ghosh said.
Ghosh explained that traditional SMART trials assign patients to treatment arms in equal numbers, even when interim data suggests some treatments are less effective. This can lead to unnecessary treatment failures.
“We have developed an adaptive randomization method that dynamically assigns patients to treatment arms based on real-time trial data by optimally changing the patient allocation ratios in favor of a better-performing treatment sequence at that point in time of the trial,” he added.
This innovation ensures more patients receive effective treatments while maintaining scientific rigor.
“By focusing on both short-term and long-term outcomes, the method will improve the entire treatment process, reducing failures and enhancing patient care,” Ghosh said.
He also noted that adaptive designs like this could encourage greater patient participation in SMART clinical trials, as patients are more likely to stay engaged when they see they are receiving tailored treatments.
The approach has significant potential for public health interventions, such as tailoring substance abuse recovery plans, and for managing other chronic diseases.
The research team is now collaborating with Indian medical institutions to conduct SMART trials for the effective management of mental health issues using traditional Indian medicines.