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IIT Guwahati Develops Adaptive Clinical Trial Method for Personalized Healthcare

Written by : Dr. Aishwarya Sarthe

February 5, 2025

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The new approach, based on Dynamic Treatment Regimes (DTRs) and SMARTs, customizes treatment plans in real-time based on individual patient responses.

Researchers at the Indian Institute of Technology (IIT) Guwahati, in collaboration with global institutions, have developed an advanced multi-stage clinical trial method to optimize personalized medical care.

The new approach, based on Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs), customizes treatment plans in real-time based on individual patient responses.

The research, conducted in partnership with Duke-NUS Medical School, the National University of Singapore, and the University of Michigan, seeks to improve treatment outcomes, reduce medication trial and error, and enhance overall healthcare efficiency.

Adaptive Trials for Improved Treatment Strategies

The methodology focuses on Dynamic Treatment Regimes (DTRs), which adjust therapies according to a patient’s evolving condition.

For instance, if a diabetes patient does not respond well to an initial drug, the DTR framework suggests switching medications or combining therapies. This method moves beyond traditional fixed regimens by incorporating intermediate outcomes, such as blood sugar fluctuations.

Multi-stage clinical trials are critical in developing effective DTRs. The SMART methodology allows researchers to test different treatment sequences to identify the most suitable therapy for each patient.

Unlike conventional trials, where treatments are assigned equally regardless of effectiveness, SMART trials reassign patients dynamically based on their responses.

Dr Palash Ghosh, Assistant Professor in the Department of Mathematics at IIT Guwahati, 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."

Adaptive Randomisation for Better Outcomes

Traditional SMART trials assign patients to different treatment arms in equal numbers, even if some treatments prove less effective.

Dr Ghosh and his team developed an adaptive randomization method that dynamically adjusts patient assignments based on real-time trial data to address this limitation.

This optimized allocation method prioritizes effective treatment sequences, ensuring that more patients receive beneficial therapies while maintaining scientific accuracy.

According to the researchers, this method improves short-term and long-term treatment outcomes, reduces failures, and makes the overall process more effective.

The study, published in the journal Biometrics, is co-authored by Dr Palash Ghosh, his research scholar Rik Ghosh (IIT Guwahati), Dr Bibhas Chakraborty (Duke-NUS Medical School), Dr Inbal Nahum-Shani, and Dr Megan E. Patrick (University of Michigan).

As a next step, the research team is collaborating with Indian medical institutions to apply SMART trials for the management of mental health conditions using traditional Indian medicines, according to the press release.


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