A/Prof Bibhas Chakraborty is a tenured Associate Professor at the Centre for Quantitative Medicine and the Signature Program in Health Services & Systems Research at Duke-NUS Medical School, an Associate Professor at the Department of Statistics and Applied Probability, National University of Singapore (NUS), and an Adjunct Associate Professor at the Department of Biostatistics and Bioinformatics, Duke University, USA. He is also affiliated to the Centre for Aging Research and Education (CARE) and the Emergency Medicine Academic Clinical Program at Duke-NUS, as well as the Institute for Applied Learning Sciences and Educational Technology (ALSET) at NUS. Previously (2014-18), he served as the Director of the Centre for Quantitative Medicine, and as the Founding Co-director of the PhD program in Quantitative Biology and Medicine (QBM) at Duke-NUS.
Bibhas completed his bachelor’s and master’s degrees respectively from the University of Calcutta and the Indian Statistical Institute in Kolkata, India. Subsequently he completed a PhD in Statistics from the University of Michigan, Ann Arbor, USA, under the supervision of Prof Susan Murphy in 2009. He then worked as an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University, USA (2009-13), prior to his move to Singapore.
He is the recipient of the prestigious Calderone Research Prize for Junior Faculty from Columbia University’s Mailman School of Public Health in 2011, and also the Young Researcher Award from the International Indian Statistical Association (IISA) in 2017. He has served as the Principal Investigator of research grants funded by the Ministry of Education (MOE), Singapore, as well as the National Institutes of Health (NIH), USA, in addition to being a co-investigator on numerous grants from various funding agencies. Over the years, he has also served as a scientific reviewer for several funding agencies, including the National Medical Research Council (NMRC) in Singapore, the NIH and the Patient-Centered Outcomes Research Institute (PCORI) in the United States, the Netherlands Organization for Scientific Research, and the French National Alliance for Life and Health Sciences, and also as an Expert Statistical Resource to the Institutional Review Board of the Singapore Health Services (SingHealth).
His 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 decision support systems for clinicians and other healthcare providers, and are particularly appealing in the context of chronic disease management. He has authored the first textbook on this cutting-edge topic. He also has expertise in modern clinical trial designs, including sequential multiple-assignment randomized trial (SMART) design for dynamic treatment regimens, various kinds of adaptive design, as well as full and fractional factorial designs in the context of multi-phase optimization strategy (MOST) for developing multi-component (behavioural) interventions. More recently, he has got deeply interested into the domain of mobile/digital health, in particular the development of just-in-time adaptive interventions (JITAIs) and the micro-randomized trials (MRTs), and also the analysis of big electronic health records data. The above research areas employ tools from artificial intelligence, including reinforcement learning and other forms of machine learning. In 2019, he has organized a mobile health workshop funded by the Institute of Mathematical Sciences, National University of Singapore.
He has served as the Principal Investigator of research grants funded by the Ministry of Education (MOE), Singapore, as well as the National Institutes of Health (NIH), USA, in addition to being a co-investigator on numerous grants from various funding agencies. Here is the link to his Google Scholar citation page.
Qian M, Chakraborty B, Maiti R and Cheung YK (2019). A sequential significance test for treatment by covariate interactions. Statistica Sinica, DOI: 10.5705/ss.202018.0451.
Ghosh P, Nahum-Shani I, Spring B, and Chakraborty B (2019). Non-inferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs). Psychological Methods, DOI: 10.1037/met0000232.
Xie F, Wu SX, Ang Y, Low LL, Matchar DB, Liu N, Ong MEH, and Chakraborty B (2019). Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study. BMJ Open, 9: e031382. DOI: 10.1136/bmjopen-2019-031382.
Xu J, Bandyopadhyay D, Mirzaei S, Michalowicz B, and Chakraborty B (2019). SMARTp: A SMART design for non-surgical treatments of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes. Biometrical Journal, DOI: 10.1002/bimj.201900027.
Chakraborty B, Maiti R, and Strecher V (2018). The effectiveness of web-based tailored smoking cessation interventions on the quitting process (Project Quit): Secondary analysis of a randomized controlled trial. Journal of Medical Internet Research, 20(6): e213.
Maiti R, Biswas A, and Chakraborty B (2018). Modelling of low count heavy tailed time series data consisting large number of zeros and ones. Statistical Methods and Applications, 27(3): 407-435.
Chakraborty B (2018). Discussion of “Optimal treatment allocations in space and time for on-line control of an emerging infectious disease” by Laber et al. Journal of the Royal Statistical Society Series C (Applied Statistics), 67(4): 743-789.
Simoneau G, Moodie EEM, Platt RW, and Chakraborty B (2018). Non-regular inference for dynamic weighted ordinary least squares: understanding the impact of solid food intake in infancy on childhood weight. Biostatistics, 19(2): 233-246.
Chakraborty B, Ghosh P, Moodie EEM, and Rush AJ (2016). Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial. Biometrics, 72(3): 865 - 876.
Ertefaie A, Shortreed S, and Chakraborty B (2016). Q-learning residual analysis: Application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia. Statistics in Medicine, 35(13): 2221 - 2234.
Matchar DB, Chei CL, Yin ZX, Koh V, Chakraborty B, Shi XM, and Zeng Y (2016). Vitamin D levels and the risk of cognitive decline in Chinese elderly: the Chinese Longitudinal Healthy Longevity Survey. Journal of Gerontology: Medical Sciences, 71(10): 1363-1368. [Duke-NUS Press Release]
Cheung YK, Chakraborty B, and Davidson K (2015). Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program. Biometrics, 71: 450 – 459.
Chakraborty B and Murphy SA (2014). Dynamic treatment regimes. Annual Review of Statistics and Its Application, 1: 447 – 464.
Chakraborty B and Moodie EEM (2013). Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer, New York. ISBN: 978-1-4614-7427-2.
Chakraborty B, Laber EB, and Zhao YQ (2013). Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. Biometrics, 69(3): 714 - 723. [R package]
Moodie EEM, Chakraborty B, and Kramer M (2012). Q-learning for estimating optimal dynamic treatment rules from observational data. Canadian Journal of Statistics, 40(4): 629 – 645.
Levin B, Thompson JLP, Chakraborty B, Levy G, MacArthur RB, and Haley EC (2011). Statistical aspects of the TNK-S2B trial of tenecteplase versus alteplase: An efficient, dose-adaptive, seamless phase II/III design. Clinical Trials, 8(4): 398 – 407.
Chakraborty B(2011). Dynamic treatment regimes for managing chronic health conditions: A statistical perspective. American Journal of Public Health, 101(1): 40 – 45.
Chakraborty B, Murphy S, and Strecher V (2010). Inference for non-regular parameters in optimal dynamic treatment regimes. Statistical Methods in Medical Research, 19(3): 317 – 343.
Chakraborty B, Collins L, Strecher V, and Murphy S (2009). Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine, 28(21): 2687 – 2708.