Directory

A/Prof Chakraborty’s primary research interest lies in developing novel statistical methods and associated study designs to facilitate data-driven precision health in a time-varying setting, often known as dynamic treatment regimens (DTRs) or adaptive interventions. Once developed, these treatment regimens can serve as data-driven decision support systems for healthcare providers. He has authored the first textbook on this cutting-edge topic that assimilated concepts from reinforcement learning, causal inference and precision medicine.

He is an expert in modern clinical and behavioural intervention trial designs, including the sequential multiple-assignment randomized trial (SMART) design for DTRs, the micro-randomized trials (MRTs) for developing just-in-time adaptive interventions (JITAIs) in mobile/digital health, full and fractional factorial designs in the context of multi-phase optimization strategy (MOST) for developing multi-component interventions, as well as various adaptive designs. In 2019, he organized a mobile health workshop funded and hosted by the Institute of Mathematical Sciences, National University of Singapore.

His other area of interest is the analysis of big electronic health records data using interpretable machine learning and other tools from artificial intelligence (AI).

His research has been supported by the Ministry of Education (MOE), the Duke-NUS Medical School, the NUS Institute of Mathematical Sciences and the A*Star from Singapore, as well as the NIH and the PCORI from the USA.

Here is his Google Scholar citation page.

A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome.

Statistics in medicine – Ghosh Palash; Yan Xiaoxi; Chakraborty Bibhas (2023)

42 (7); 1096-1111

Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health.

American journal of public health – Liu Xueqing; Deliu Nina; Chakraborty Bibhas (2023)

113 (1); 60-69

The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments.

American journal of public health – Wang Xinru; Chakraborty Bibhas (2023)

113 (1); 49-59

A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification.

Statistical methods in medical research – Maiti Raju; Li Jialiang; Das Priyam; Liu Xueqing; Feng Lei; Hausenloy Derek J; Chakraborty Bibhas (2023)

32 (2); 242-266

Shapley variable importance cloud for interpretable machine learning.

Patterns (New York, N.Y.) – Ning Yilin; Ong Marcus Eng Hock; Chakraborty Bibhas; Goldstein Benjamin Alan; Ting Daniel Shu Wei; Vaughan Roger; Liu Nan (2022)

3 (4);

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Journal of biomedical informatics – Xie Feng; Ning Yilin; Yuan Han; Goldstein Benjamin Alan; Ong Marcus Eng Hock; Liu Nan; Chakraborty Bibhas (2022)

125

A sequential significance test for treatment by covariate interactions

Statistica Sinica – Qian M, Chakraborty B, *Maiti R, and Cheung YK  (2021)

31 (3); 1353-1374

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

Journal of the American Medical Informatics Association : JAMIA – Figueroa Caroline A; Aguilera Adrian; Chakraborty Bibhas; Modiri Arghavan; Aggarwal Jai; Deliu Nina; Sarkar Urmimala; Jay Williams Joseph; Lyles Courtney R (2021)

28 (6); 1225-1234

COVID-19 in India: Statewise Analysis and Prediction.

JMIR public health and surveillance – Ghosh Palash; Ghosh Rik; Chakraborty Bibhas (2020)

6 (3);

Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Psychological methods – Ghosh Palash; Nahum-Shani Inbal; Spring Bonnie; Chakraborty Bibhas (2020)

25 (2); 182-205

SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes.

Biometrical journal. Biometrische Zeitschrift – Xu Jing; Bandyopadhyay Dipankar; Salehabadi Sedigheh Mirzaei; Michalowicz Bryan; Chakraborty Bibhas (2020)

62 (2); 282-310

Non-regular inference for dynamic weighted ordinary least squares: understanding the impact of solid food intake in infancy on childhood weight.

Biostatistics (Oxford, England) – Simoneau Gabrielle; Moodie Erica E M; Platt Robert W; Chakraborty Bibhas (2018)

19 (2); 233-246

Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial.

Biometrics – Chakraborty Bibhas; Ghosh Palash; Moodie Erica E M; Rush A John (2016)

72 (3); 865-76

Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program.

Biometrics – Cheung Ying Kuen; Chakraborty Bibhas; Davidson Karina W (2015)

71 (2); 450-9

Dynamic Treatment Regimes.

Annual review of statistics and its application – Chakraborty Bibhas; Murphy Susan A (2014)

1 447-464

Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine.

Springer, New York – Chakraborty B and Moodie EEM (2013)

Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.

Biometrics – Chakraborty Bibhas; Laber Eric B; Zhao Yingqi (2013)

69 (3); 714-23

Q-learning for estimating optimal dynamic treatment rules from observational data.

The Canadian journal of statistics = Revue canadienne de statistique – Moodie Erica E M; Chakraborty Bibhas; Kramer Michael S (2012)

40 (4); 629-645

Inference for non-regular parameters in optimal dynamic treatment regimes.

Statistical methods in medical research – Chakraborty Bibhas; Murphy Susan; Strecher Victor (2010)

19 (3); 317-43

Developing multicomponent interventions using fractional factorial designs.

Statistics in medicine – Chakraborty Bibhas; Collins Linda M; Strecher Victor J; Murphy Susan A (2009)

28 (21); 2687-708