Biography
Multi-Disciplinary Training Area
Artificial Intelligence and Emerging Technologies in Medicine [AIET]
Artificial Intelligence and Emerging Technologies in Medicine [AIET]
Madhu Mazumdar, PhD is Director of the Institute for Healthcare Delivery Science at the Mount Sinai Health System and is a Professor of Biostatistics at the Center of Biostatistics, Department of Population Health Science and Policy. She also directs the Biostatistics Core of Tisch Cancer Institute.
Dr. Mazumdar developed methodologies for detecting publication bias in meta-analysis; for adjusting selection bias in clinical trials; for allowing interim looks at data in clinical trials comparing diagnostic tests; for developing and validating quality of life questionnaire; and for estimating misclassification rate of responders when oncologic response criteria were changed. Her collaborative research resulted in personalized treatment regimen for various cancer types and orthopedic surgeries. Her work also changed guidelines for staging cancer and practice guidelines for use of anesthetics. She developed innovative interdisciplinary educational and research programs, in collaboration with various clinical departments that increased productivity through grantsmanship and augmented clinical revenue through improved care delivery. Working within inter-disciplinary teams, she has trained more than 50 biostatisticians, epidemiologists, and clinical researchers, many of whom are leading quantitative science units today.
Dr. Mazumdar developed methodologies for detecting publication bias in meta-analysis; for adjusting selection bias in clinical trials; for allowing interim looks at data in clinical trials comparing diagnostic tests; for developing and validating quality of life questionnaire; and for estimating misclassification rate of responders when oncologic response criteria were changed. Her collaborative research resulted in personalized treatment regimen for various cancer types and orthopedic surgeries. Her work also changed guidelines for staging cancer and practice guidelines for use of anesthetics. She developed innovative interdisciplinary educational and research programs, in collaboration with various clinical departments that increased productivity through grantsmanship and augmented clinical revenue through improved care delivery. Working within inter-disciplinary teams, she has trained more than 50 biostatisticians, epidemiologists, and clinical researchers, many of whom are leading quantitative science units today.
Madhu Mazumdar, PhD is Director of the Institute for Healthcare Delivery Science at the Mount Sinai Health System and is a Professor of Biostatistics at the Center of Biostatistics, Department of Population Health Science and Policy. She also directs the Biostatistics Core of Tisch Cancer Institute.
Dr. Mazumdar developed methodologies for detecting publication bias in meta-analysis; for adjusting selection bias in clinical trials; for allowing interim looks at data in clinical trials comparing diagnostic tests; for developing and validating quality of life questionnaire; and for estimating misclassification rate of responders when oncologic response criteria were changed. Her collaborative research resulted in personalized treatment regimen for various cancer types and orthopedic surgeries. Her work also changed guidelines for staging cancer and practice guidelines for use of anesthetics. She developed innovative interdisciplinary educational and research programs, in collaboration with various clinical departments that increased productivity through grantsmanship and augmented clinical revenue through improved care delivery. Working within inter-disciplinary teams, she has trained more than 50 biostatisticians, epidemiologists, and clinical researchers, many of whom are leading quantitative science units today.
Dr. Mazumdar developed methodologies for detecting publication bias in meta-analysis; for adjusting selection bias in clinical trials; for allowing interim looks at data in clinical trials comparing diagnostic tests; for developing and validating quality of life questionnaire; and for estimating misclassification rate of responders when oncologic response criteria were changed. Her collaborative research resulted in personalized treatment regimen for various cancer types and orthopedic surgeries. Her work also changed guidelines for staging cancer and practice guidelines for use of anesthetics. She developed innovative interdisciplinary educational and research programs, in collaboration with various clinical departments that increased productivity through grantsmanship and augmented clinical revenue through improved care delivery. Working within inter-disciplinary teams, she has trained more than 50 biostatisticians, epidemiologists, and clinical researchers, many of whom are leading quantitative science units today.