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Research Papers

* A student/post-doctoral fellow/mentee of Dr. Chakraborty during the time of the work

Published Articles in Peer-Reviewed Journals

    1. *Liu X, Qian T, Bell L, and Chakraborty B (2024). Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes. Biometrics, in press. [arXiv]

    2. Aravindhan A, Fenwick EK, Chan AWD, Man RYK, Tan NC, Wong WT, Soo WF, Lim SW, Wee SY-M, Sabanayagam C, Finkelstein E, Tan G, Chakraborty B, Acharyya S, Shyong TE, Scanlon P, Wong TY, and Lamoureux EL (2024). Extending the diabetic retinopathy screening interval in Singapore: methodology and preliminary findings of a cohort study. BMC Public Health, 24: 786.

    3. Paul E, Chakraborty B, Sikorski A, and Ghosh S (2024). A framework for testing non-inferiority in three-arm, sequential, multiple assignment, randomized trials. Statistical Methods in Medical Research, DOI: 10.1177/09622802241232124.

    4. Avalos MRA, *Xu J, Figueroa CA, Haro-Ramos A, Chakraborty B, and Aguilera A (2024). The effect of cognitive behavioral therapy text messages on mood: A micro-randomized trial. PLOS Digital Health, DOI: 10.1371/journal.pdig.0000449.

    5. *Wang X, *Deliu N, Narita Y, and Chakraborty B (2024). Incorporating participants’ welfare into sequential multiple assignment randomized trials. Biometrics, 80(1): ujad004. DOI: 10.1093/biomtc/ujad004. [Free Access]

    6. Saberi P, Stoner MCD, Mccuistian C, Balaban C, Ming K, Wagner D, Chakraborty B, Smith L, Sukhija-Cohen A, Neilands TB, Gruber V, and Johnson MO (2023). iVY: Protocol for a randomized clinical trial to test the effect of a technology-based intervention to improve virologic suppression among young adults with HIV in the United States. BMJ Open, 13(10):e077676.

    7. Li S, Ning Y, Ong MEH, Chakraborty B, Hong C, Xie F, Yuan H, Liu M, Buckland D,  Chen Y, and Liu N (2023). FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics, 146: 104485.

    8. Nisa CF, *Yan X, Chakraborty B, Leander P, and Belanger JJ (2023). COVID-19 may have increased global support for universal health coverage: Multi-country observational study. Frontiers in Public Health, 11: 1213037.

    9. Li S, Liu P, Nascimento GG, *Wang X, Chakraborty B, Leite FRM, Xie F, Ning Y, Ong MEH, Haddadi H, Teo ZL, Ting DSW, Peres MA, and Liu N (2023). Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. Journal of the American Medical Informatics Association, DOI: 10.1093/jamia/ocad170.

    10. Mitra S, Kroeger CM, *Xu J, Avery L, Masedunskas A, Cassidy S, Wang T, Hunyor I, Wilcox I, Huang R, Chakraborty B, and Fontana L (2023). Testing the effects of app-based motivational messages on physical activity and resting heart rate via smartphone app compliance in patients with vulnerable coronary artery plaques: Protocol for a micro-randomized trial. JMIR Research Protocols, 12: e46082.

    11. *Xu J, *Yan X, Figueroa C, Williams JJ, and Chakraborty B (2023). A flexible micro-randomized trial design and sample size considerations. Statistical Methods in Medical Research, 32(9):1766-1783. [arXiv]

    12. Liu M, Li S, Yuan H, Ong MEH, Ning Y, Xie F, Saffari SE, Shang Y, Volovici V, Chakraborty B, and Liu N (2023). Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine, 142: 102587.

    13. *Leong U and Chakraborty B (2023). Participant engagement in microrandomized trials of mHealth interventions: Scoping review. JMIR mHealth and uHealth, 11: e44685. [PsyArXiv]
    14. *Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan RDV, Chakraborty B, and Liu N (2023). A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols, 4(2): 102302.

    15. Yap J, Dziak J, *Maiti R, Lynch K, McKay J, Chakraborty B, and Nahum-Shani I (2023). Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed  count outcomes. Statistical Methods in Medical Research, 32(7): 1267 – 1283.

    16. *Ghosh P, *Yan X, and Chakraborty B (2023). A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome. Statistics in Medicine, 42(7): 1096 – 1111. [arXiv]

    17. Chattopadhyay S, Ghosh D, Maiti R, Das S, Biswas A, and Chakraborty B (2023). A Study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis. Epidemiologic Methods, DOI: 10.1515/em-2022-0113.

    18. *Maiti R, Li J, Das P, *Liu X, Feng L, Hausenloy D, and Chakraborty B (2023). A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification. Statistical Methods in Medical Research, 32(2): 242 – 266.
    19. *Liu X, Deliu N, and Chakraborty B (2023). Micro-randomized trials: developing just-in-time adaptive interventions for better public health. American Journal of Public Health, 113(1): 60 – 69. (with commentary)  [Open Access]

      [Featured on the cover page of the American Journal of Public Health.]

    20. *Wang X and Chakraborty B (2023). The sequential multiple assignment randomized trial for controlling infectious diseases: A review of recent developments. American Journal of Public Health, 113(1): 49 – 59. (with commentary 1 and commentary 2) [Open Access]

      [Featured on the cover page of the American Journal of Public Health.]

      [Selected as a Continual Medical Education (CME) offering of the American Journal of Public Health.] 

    21. Das P, De D, *Maiti R, Kamal M, Hutcheson KA, Fuller CD, Chakraborty B, and Peterson CB (2022). Estimating the optimal linear combination of predictors using spherically constrained optimization. BMC Bioinformatics, 23 (Suppl 3), 436.

    22. Sung SC, Lim LEC, Lim SH, Finkelstein EA, Lim SHC, Annathurai A, Chakraborty B, Strauman T, Pollack MH, and Ong MEH (2022). Protocol for a multi-site randomized controlled trial of a stepped-care intervention for emergency department patients with panic-related anxiety. BMC Psychiatry, 22(1): 795.

    23. Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, and Liu N (2022). AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes. BMC Medical Research Methodology, 22: 286.

    24. *Xie F, Zhou J, Lee JW, Tan M, Li S, Rajnthern L, Chee ML, Chakraborty B, Wong AI, Dagan A, Ong MEH, Gao F, and Liu N (2022). Benchmarking emergency department prediction models with machine learning and public electronic health records. Scientific Data, 9: 658.

    25. Ning Y, Li S, Ong MEH, *Xie F, Chakraborty B, Ting DSW, and Liu N (2022). A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health1(6): e0000062.

    26. Ang Y, Ong MEH, *Xie F, Teo SH, Choong L, Koniman R, Chakraborty B, Ho AFW, and Liu N (2022). Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports, 12:7111.

    27. Yuan H, *Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein B, Chakraborty B, and Liu N (2022). AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics129:104072.

    28. *Liew FT, Ghosh P, and Chakraborty B (2022). Accounting for the role of asymptomatic patients in understanding the dynamics of the COVID-19 pandemic: A case study from Singapore. Epidemiologic Methods, 11(s1): 20210031.

    29. *Yan X, Dunne D, Impey SG, Cunniffe B, Lefevre CE, Mazorra R, Morton JP, Tod D, Close GL, Murphy R, and Chakraborty B (2022). A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior. Contemporary Clinical Trials Communications, 26: 100899.

    30. *Xie F, Liu N, Yan L, Ning Y, Lim KK, Gong C, Kwan YH, Ho AFW, Low LL, Chakraborty B, and Ong MEH (2022). Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine, 45: 101315.

    31. Ning Y, Ong MEH, Chakraborty B, Goldstein BA, Ting DSW, Vaughan RD, and Liu N (2022). Shapley variable importance cloud for interpretable machine learning. Patterns, 3(4): 100452.

    32. Shin S, Chakraborty B, *Yan X, van Dam RM, and Finkelstein EA (2022). Evaluation of combinations of nudging, pricing, and labelling strategies to improve diet quality: A virtual grocery store experiment employing Multiphase Optimization Strategy. Annals of Behavioral Medicine, 56(9): 933 – 945.

    33. *Xie F, Yuan H, Ning Y, Ong MEH, Feng M, Hsu W, Chakraborty B, and Liu N (2022). Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics, 126:103980.
    34. Liu N, *Xie F, Siddiqui FJ, Ho AFW, Chakraborty B, Nadarajan GD, Tan KBK, and Ong MEH (2022). Leveraging large-scale electronic health records and interpretable machine learning for clinical decision making at the emergency department: Protocol for system development and validation. JMIR Research Protocols11(3): e34201.

    35. *Xie F, Ong MEH, Ning Y, Goldstein BA, Liu N, and Chakraborty B (2022). AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics, 125: 103959. [Free Access]

    36. Tan YK, Teo EPS, Saffari SE, Xin X, Chakraborty B, Ng CT, Thumboo J (2022). A musculoskeletal ultrasound program as an intervention to improve disease modifying anti-rheumatic drugs adherence in rheumatoid arthritis: a randomized controlled trial. Scandinavian Journal of Rheumatology, 51(1): 1 – 9.
    37. Aguilera A, Hernandez-Ramos R, Haro-Ramos AY, Boone CE, Luo TC, *Xu J, Chakraborty B, Karr C, Darrow SM, Figueroa CA (2021). A text messaging intervention (StayWell at Home) to counteract depression and anxiety during COVID-19 social distancing: Pre-post study. JMIR Mental Health, 8(11): e25298.

    38. *Yan X, Matchar DB, Sivapragasam N, Ansah JP, *Goel A, and Chakraborty B (2021). Sequential multiple assignment randomized trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study. BMC Medical Research Methodology, 21: 200.

    39. Matchar DB, Young SHY, Sim R, Yu CJY, *Yan X, De Silva DA, and Chakraborty B (2021). Incentives for uptake of and adherence to outpatient stroke rehabilitation services: A three-arm randomized controlled trial. Archives of Physical Medicine and Rehabilitation, S0003-9993(21)01422-2.

    40. *Xie F, Ong MEH, Liew JNMH, Tan KBK, Ho AFW, Nadarajan GD, Low LL, Kwan YH, Goldstein BA, Matchar DB, Chakraborty B, and Liu N (2021). Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Network Open, 4(8): e2118467.

    41. Qian M, Chakraborty B, *Maiti R, and Cheung YK (2021). A sequential significance test for treatment by covariate interactions. Statistica Sinica, 31(3): 1353 – 1374. [arXiv]

    42. Figueroa C, *Deliu N, Chakraborty B, *Xu J, Modiri A, Aggarwal J, Williams JJ, Lyles C, and Aguilera A (2021). Daily motivational text-messages to promote physical activity in university students: Results from a micro-randomized trial. Annals of Behavioral Medicine, 56(2): 212 – 218.

    43. Mahar R, McGuinness M, Chakraborty B, Carlin J, Ijzerman M, and Simpson J (2021). A scoping review of studies using observational data to optimise dynamic treatment regimens. BMC Medical Research Methodology, 21: 39.

    44. Figueroa C, Aguilera A, Chakraborty B, Modiri A, Aggarwal J, *Deliu N, Sarkar U, Williams JJ, and Lyles CR  (2021). Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Journal of the American Medical Informatics Association, 28(6): 1225-1234.

    45. *Yan X, Ghosh P, and Chakraborty B (2021). Sample size calculation based on precision for pilot sequential multiple assignment randomised trial (SMART). Biometrical Journal, 63(2): 247-271. [R Shiny App]

    46. Figueroa C, Hernandez-Ramos R, Boone C, Gomez L, Yip V, Luo T, Sierra V, *Xu J, Chakraborty B, Darrow SM, and Aguilera A (2021). A text-messaging intervention for coping with social distancing during COVID-19 (StayWell at Home): protocol for a randomized controlled trial. JMIR Research Protocols, 10(1): e23592.

    47. Chakraborty B (2020). Discussion of “Statistical Remedies for Medical Researchers” by Peter F. Thall. International Statistical Review, 88(3): 802-804.

    48. Ghosh P, Ghosh R, and Chakraborty B (2020). COVID-19 in India: State-wise analysis and prediction. JMIR Public Health and Surveillance, 6(3): e20341. [medRxiv] [R Shiny App] [Media Coverage]

    49. *Xie F, Chakraborty B, Ong MEH, Goldstein BA, and Liu N (2020). AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics, 8(10): e21798. [R Software Package]

    50. Aguilera A, Figueroa CA, Hernandez-Ramos R, Sarkar U, Cemballi AG, Gomez-Pathak L, Miramontes J, Avila-Garcia P, Tov EY, Chakraborty B, *Yan X, *Xu J, Modiri A, Aggarwal J, Williams JJ, and Lyles CR (2020).  mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE study. BMJ Open

    51. *Ghosh P, Nahum-Shani I, Spring B, and Chakraborty B (2020). Non-inferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs). Psychological Methods, 25(2): 182-205.   [arXiv] [R Shiny App]

    52. *Xu J, Bandyopadhyay D, *Mirzaei S, Michalowicz B, and Chakraborty B (2020). SMARTp: A SMART design for non-surgical treatments of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes. Biometrical Journal, 62(2): 282-310. [R Software Package] [R Shiny App]

      [Selected as a high-impact publication in the Clinical Research category, and included in the NIH/NIDCR Director’s Report to the US National Advisory Dental and Craniofacial Research Council (p.12) in Jan 2020]

    53. Liu N, Guo D, Koh ZX, Ho AFW, *Xie F, Tagami T, Sakamoto JT, Pek PP, Chakraborty B, Lim SH, Tan JWC, and Ong MEH (2020). Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department. BMC Cardiovascular Disorders, 20:168. [biorxiv]

    54. Mathur R, de Korne DF, Wong TY, Hwee DTT, Chiang PP, Wong E, Chakraborty B, and Lamoureux EL (2019). Shared care for patients with diabetes at risk of retinopathy: A feasibility trial. International Journal of Integrated Care, 19(3): 18.

    55. *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.

    56. Leung Y-Y, Lim Z, Fan Q, Wylde V, Xiong S, Yeo SJ, Lo NN, Chong HC, Yeo W, Tan MH, Chakraborty B, Wong S B-S, and Thumboo J (2019). Pre-operative pressure pain thresholds do not meaningfully explain satisfaction or improvement in pain after knee replacement: A cohort study. Osteoarthritis and Cartilage, 27(1): 49-58.
    57. *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.

    58. 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): 773-774.

    59. 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.

    60. 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.

    61. Wilkins J, *Ghosh P, Vivar J, Chakraborty B, and Ghosh S (2018). Exploring the associations between systemic inflammation, obesity and healthy days: a health related quality of life (HRQOL) analysis of NHANES 2005-2008. BMC Obesity, 5:21.

    62. Northridge ME, Chakraborty B, *Mirzaei Salehabadi S, Metcalf SS, Kunzel C, Greenblatt A, Borrell L, Cheng B, Marshall S, and Lamster I (2018). Does Medicaid coverage modify the relationship between glycemic status and teeth present in older adults? Journal of Health Care for the Poor and Underserved, 29(4): 1509-1528.

    63. Leung Y-Y, Haaland B, Huebner J, Wong BS, Tjai M, Wang C, Chowbay B, Thumboo J, Chakraborty B, Tan MH, and Kraus VB (2018). Colchicine lack of effectiveness in symptom and inflammation modification in knee osteoarthritis (COLKOA): a randomized controlled trial. Osteoarthritis and Cartilage; 26(5): 631-640.

    64. Ong NWR, Ho AFW, Chakraborty B, Fook-Chong S, Pashupathi Y, Lian S, Xin X, Poh J, Chiew KKY, and Ong MEH (2018). Utility of a Medical Alert Protection System compared to telephone follow-up only for home-alone elderly presenting to the Emergency Department – A randomized controlled trial. American Journal of Emergency Medicine, 36(4): 594-601.

    65. Bulluck H, *Maiti R, Chakraborty B, Candilio L, Clayton T, Jenkins D, Kolvekar S, Laing C, Nicholas J, Pepper J, Yellon D, and Hausenloy DJ (2018). Neutrophil gelatinase-associated lipocalin prior to cardiac surgery predicts acute kidney injury and mortality. Heart, 104: 313-317.
    66. Goh D, de Korne D, Ho H, Mathur R, Chakraborty B, Van Hai N, Shamira P, Tin A, Wong TY, and Lamoureux E (2018). Shared cared for stable glaucoma patients: economic benefits and patient-centered outcomes of a feasibility trial. Journal of Glaucoma; 27(2): 70-75.

    67. Chakraborty B, Widener MJ, *Mirzaei Salehabadi S, Northridge ME, Kum SS, Jin Z, Kunzel C, Palmer HD, and Metcalf SS (2017). Estimating peer density effects on oral health for community-based older adults. BMC Oral Health, 17(1): 166.

    68. Ganda A, Yvan-Charvet L, Zhang Y, Lai EJ, Regunathan-Shenk R, Hussain FN, Avasare R, Chakraborty B, Febus AJ, Vernocchi L, Lantigua R, Wang Y, Shi X, Hsieh J, Murphy AJ, Wang N, Bijl N, Gordon KM, de Miguel MH, Singer JR, Hogan J, Cremers S, Magnusson M, Melander O, Gerszten RE, and Tall AR (2017). Plasma metabolite profiles, cellular cholesterol efflux, and non-traditional cardiovascular risk in patients with CKD. Journal of Molecular and Cellular Cardiology, 112: 114 – 122.

    69. Kalkhoran SB, Hall A, White I, Cooper J, Qiao F, Ong SB, Cabrera-Fuentes H, Hernandez S, Chinda K, Chakraborty B, Dorn G, Yellon D, and Hausenloy D (2017). Assessing the effects of Mitofusin 2 deficiency in the adult heart using 3D electron tomography. Physiological Reports, 5(17): e13437.

    70. Kalkhoran SB, Munro P, Qiao F, Ong S, Hall AR, Cabrera-Fuentes H, Chakraborty B, Boisvert WA, Yellon DM, and Hausenloy DJ (2017). Unique morphological characteristics of mitochondrial subtypes in the heart: the effect of ischemia and ischemic preconditioning. Discoveries, 5(1): e71.

    71. Northridge ME, Shedlin MG, Schrimshaw EW, Estrada I, De La Cruz L, Peralta R, Birdsall SB, Metcalf SS, Chakraborty B, and Kunzel C (2017). Recruitment of racial/ethnic minority older adults through community sites for focus group discussions. BMC Public Health, 17:563.

    72. 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.

    73. 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.

    74. Northridge ME, Kum SS, Chakraborty B, Port Greenblatt A, Marshall SE, Wang H, Kunzel C, and Metcalf SS (2016). Third places for health promotion with older adults: using the Consolidated Framework for Implementation Research to enhance program implementation and evaluation. Journal of Urban Health, 93(5): 851 – 870.

    75. Matchar DB, Chei C-L, Yin Z-X, Koh V, Chakraborty B, Shi X-M, 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] [Media Coverage]

    76. Khaw KBC, Choi RH, Kam JH, Chakraborty B, and Chow PKH (2016). Interval increase in the prevalence of symptomatic cholelithiasis-associated nonalcoholic fatty liver disease (NAFLD) over a 10-year period in an Asian population. Singapore Medical Journal, 58(12): 703-707. [Media Coverage]

    77. 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(2): 450 – 459.

    78. Leung Y-Y, Thumboo J, Wong BS, Haaland B, Chowbay B, Chakraborty B, Tan MH, and Kraus VB (2015). Colchicine effectiveness in symptom and inflammation modification in knee osteoarthritis (COLKOA): study protocol for a randomized controlled trial. Trials, 16: 200.

    79. Northridge M, *Yu C, Chakraborty B, Port A, Mark J, Golembeski C, Cheng B, Kunzel C, Metcalf S, Marshall S, and Lamster I (2015). A community-based oral public health approach to promote health equity. American Journal of Public Health, 105(3S): 459S – 465S.

    80. Chew LC, Maceda-Galang LM, Tan YK, Chakraborty B, and Thumboo J. (2015). Pneumocystis jirovecii pneumonia in patients with autoimmune disease on high dose glucocorticoid. Journal of Clinical Rheumatology, 21(2): 72 – 75.

    81. Chakraborty B, Laber EB, and Zhao YQ (2014). Inference about the expected performance of a data-driven dynamic treatment regime. Clinical Trials, 11: 408 – 417.

    82. Chakraborty B and Murphy SA (2014). Dynamic treatment regimes. Annual Review of Statistics and Its Application, 1: 447 – 464.

      [This article is among the most highly cited articles published in this journal.]
    83. Mackay-Wiggan J, Marji J, Walt JG, Campbell A, Coppola C, Chakraborty B, Hollander DA, Schiffman R, and Whitcup S (2014). Topical Cyclosporine versus Emulsion vehicle for the treatment of brittle nails: A randomized controlled pilot study. Journal of Drugs in Dermatology, 13(10): 1232 – 1239.

    84. Thompson BJ, Furniss M, Marji J, Ulerio G, *Zhao W, Chakraborty B, and Mackay-Wiggan J (2014). An oral phosphodiesterase inhibitor (Apremilast) for inflammatory rosacea in adults: A pilot study. JAMA Dermatology, 150(9): 1013 – 1014.

    85. Widener MJ, Northridge M, Chakraborty B, Marshall S, Lamster I, Kum S, and Metcalf S (2014). Patterns of chronic conditions in older adults: Exploratory spatial findings from the ElderSmile program. American Journal of Preventive Medicine, 46(6): 643 – 648.

    86. 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 Software Package]

    87. Metcalf S, Northridge M, Widener MJ, Chakraborty B, Marshall S, and Lamster I (2013). Modeling social dimensions of oral health among older adults in urban environments. Health Education & Behavior, 40(1S): 63S – 73S.

    88. Maurer MS, Teruya S, Chakraborty B, Helmke S, and Mancini D (2013). Treating anemia in older adults with heart failure with a preserved ejection fraction (HFPEF) with Epoetin Alfa: Single blind randomized clinical trial of safety and efficacy. Circulation: Heart Failure, 6(2): 254 – 263. 

    89. 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. 

    90. Widener MJ, Metcalf S, Northridge M, Chakraborty B, Marshall S, and Lamster I (2012). Exploring the role of peer density in the self-reported oral health outcomes of older adults: A kernel density based approach. Health and Place, 18(4): 782 – 788.

    91. Northridge M, Chakraborty B, Kunzel C, Metcalf S, Marshall S, and Lamster I (2012). What contributes to self-rated oral health among community-dwelling older adults? Findings from the ElderSmile program. Journal of Public Health Dentistry, 72(3): 235 – 245.

    92. Northridge M, Ue F, Borrell L, De La Cruz L, Chakraborty B, Bodnar S, Marshall S, and Lamster I (2012). Tooth loss and dental caries in community-dwelling older adults in northern Manhattan. Gerodontology, 29(2): e464 – 473.

    93. 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.

    94. Chakraborty B (2011). Dynamic treatment regimes for managing chronic health conditions: A statistical perspective. American Journal of Public Health, 101(1): 40 – 45.

    95. Chakraborty B, Murphy SA, and Strecher V (2010). Inference for non-regular parameters in optimal dynamic treatment regimes. Statistical Methods in Medical Research, 19(3): 317 – 343. 

    96. Chakraborty B, Collins L, Strecher V, and Murphy SA (2009). Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine, 28(21): 2687 – 2708.

    97. Collins L, Chakraborty B, Murphy SA, and Strecher V (2009). Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions. Clinical Trials, 6(1): 5 – 15.

    98. Zick S, Schwabl H, Flower A, Chakraborty B, and Hirschkorn K (2009). Unique aspects of herbal whole system research. Explore: The Journal of Science and Healing, 5(2): 97 – 103.
    99. Nair V, Strecher V, Fagerlin A, Ubel P, Resnicow K, Murphy SA, Little R, Chakraborty B, and Zhang A (2008). Screening experiments and the use of fractional factorial designs in behavioral intervention research. American Journal of Public Health, 98: 1354 – 1359.

    100. Strecher V, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, Greene S, Couper M, Carlier C, Wiese C, Little R, Pomerleau C, and Pomerleau O (2008). The role of engagement in a tailored web-based smoking cessation program: Results of a randomized trial. Journal of Medical Internet Research, 10(5): e36.

    101. Strecher V, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, Greene S, Collins L, Carlier C, Wiese C, Little R, Pomerleau C, and Pomerleau O (2008). Web-based smoking cessation components and tailoring depth: Results of a randomized trial. American Journal of Preventive Medicine, 34(5): 373 – 381.

Conference Paper

  • Chakraborty B, Strecher V, and Murphy S (2008). Bias correction and confidence intervals for fitted Q-iteration. Neural Information Processing Systems (NIPS) workshop on “Model Uncertainty and Risk in Reinforcement Learning”. [pdf]

In The Pipeline

  • *Deliu N, Williams JJ, and Chakraborty B (2024+). Reinforcement learning in modern biostatistics: Constructing optimal adaptive interventions. Under Review. [arXiv]

  • *Liu X, Deliu N, Chakraborty T, Bell L, and Chakraborty B (2024+). Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. Under Review. [arXiv]

  • Ghosh R, Chakraborty B, Nahum-Shani I, Patrick ME, and Ghosh P (2024+). Optimal adaptive SMART designs with binary outcomes. Under Review. [arXiv]

  • Naik SM, Chakraborty T, Hadid A, and Chakraborty B (2024+). Skew probabilistic neural networks for learning from imbalanced data. Under Review. [arXiv]

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