Chaya Moskowitz, PhD

Associate Attending Biostatistician
Pictured: Chaya Moskowitz
Office Phone:
646-735-8117
Office Fax:
646-735-0010
Education:
University of Washington

Current Research Interests

Dr. Moskowitz's research interests involve developing and evaluating medical tests such as risk prediction models and radiographic imaging tools that can be used to detect disease and predict future outcomes. Within this framework, her work is primarily focused on identifying individuals at high risk of health problems associated with cancer and its treatment. She is the recipient of a grant from the National Institutes of Health to develop a model that predicts the risk of breast cancer in females who were treated with chest radiation for a pediatric malignancy. Complementing this work, Dr. Moskowitz is involved in collaborative work in two main areas. First, she collaborates with colleagues in the cancer survivorship program in researching issues pertinent to survivors of cancer and has contributed to the design and analysis of studies in this area. Second, she collaborates with investigators in the Department of Radiology and has worked on the design and analysis of a variety of studies assessing different imaging modalities and potential biomarkers. In conjunction with this work, Dr. Moskowitz continues to be interested in statistical methods for developing and assessing medical tests.

Publications by Chaya Moskowitz

Moskowitz CS, Pepe MS. Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes. Biostatistics 2004;5:113-127.

Moskowitz CS, Pepe MS. Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome. Statistics in Medicine 2004;23:1555-1570.

Moskowitz CS, Pepe MS. Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clinical Trials. 2006 June; 3:272-279.

Moskowitz CS, Seshan VE, Riedel ER, Begg CB. Estimating the empirical Lorenz curve and Gini coefficient in the presence of error with nested data. Statistics in Medicine. 2008 Jul; 27(16): 3191-3208.

Moskowitz CS, Jia X, Schwartz LH, Gönen M. A simulation study to evaluate the impact of the number of lesions measured on response assessment. Eur J Cancer. 2009 Jan; 45(2): 300-310.