Chaya Moskowitz, PhD

Assistant Attending Biostatistician
Office Phone:
646-735-8117
Office Fax:
646-735-0010
Education:
University of Washington

Current Research Interests

Dr. Moskowitz's primary methodological research interests are concentrated on statistical methods for developing and evaluating medical tests. The methodology that she has proposed and continues to work on can be used to assess the ability of instruments such as biomarkers, risk prediction models, and radiographic imaging tools to detect disease and predict future outcomes. Within this framework, her current work is focused on two particular applications. She is studying statistical issues related to how doctors and researchers use imaging modalities to assess patient response to an anti-cancer therapeutic agent. She is also 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 her methodological work, Dr. Moskowitz is involved in collaborative work in two main areas. First, she collaborates with investigators in the Department of Radiology. She has worked on the design and analysis of a variety of studies assessing different imaging modalities and potential biomarkers. She also works together 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.

Publications by Chaya Moskowitz

Selected Publications

  1. Moskowitz CS, Pepe MS. Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes. Biostatistics 2004;5:113-127.
  2. 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.
  3. 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.
  4. 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.
  5. 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.