Hormuzd A. Katki, Ph.D.
|Organization:||National Cancer InstituteDivision of Cancer Epidemiology & Genetics, Biostatistics Branch|
|Address:||NCI Shady GroveRoom 7E606|
Hormuzd A. Katki received a B.S. in math from the University of Chicago and an M.S. in statistics from Carnegie-Mellon University. He received a Ph.D in biostatistics from Johns Hopkins University in 2006, where he received the Margaret Merrell Award for research by a biostatistics doctoral student. Dr. Katki joined NCI in 1999, became a principal investigator in 2009, and was appointed senior investigator upon receiving NIH scientific tenure in 2015.
Dr. Katki’s research focuses on understanding how epidemiologic findings could be used to prevent cancer in individuals and in populations. In particular, he develops and applies quantitative methods to both identify and answer the most pressing epidemiologic questions for advancing cancer prevention. He is particularly interested in developing risk-based approaches to cancer screening. If you are interested in a fellowship with Dr. Katki, please see our research training opportunities.
Dr. Katki has been an integral part of efforts to develop the epidemiologic evidence underlying new cervical cancer screening guidelines. In particular, he led a team that calculated cervical cancer risks for women with different combinations of HPV, Pap, and biopsy test results over time, using data on 1.4 million women at Kaiser Permanente Northern California (KPNC). These risks enabled guidelines to ensure “equal management of women at equal risk of cancer”. The resulting 2012 American Society for Colposcopy and Cervical Pathology Consensus Guidelines and the eight reports with the supporting data from the KPNC study were published in a 2013 supplement of the Journal of Lower Genital Tract Disease.
Dr. Katki is currently developing risk calculations that incorporate new technologies, such as primary HPV testing, HPV genotyping, and HPV vaccination. Consequently, more guidelines will have to be developed for these new technologies, and screening will become ever more complex for clinicians. In a Lancet Oncology paper, Dr. Katki and colleagues articulated a vision where risk calculations would be directly presented to clinicians for simplified and consistent application of risk-based guidelines. Dr. Katki and colleagues are collaborating with experts in risk communication and translational medicine to find constructive ways in which the risk calculations could be made directly available to clinicians.
Dr. Katki is also developing dynamic statistical models of cervical cancer risk. A dynamic model could estimate risks for any particular unique medical history. However, such models require complete knowledge about the natural history of HPV infection and the epidemiology of cervical cancer precursors. Dr. Katki is working on the significant statistical, epidemiologic, and translational challenges involved with developing such models.
Dr. Katki is helping develop evidence to better determine smokers’ benefits and harms from undergoing CT lung cancer screening. In the National Lung Screening Trial (NLST), he and his colleagues developed a model to estimate a smoker’s risk of dying from lung cancer, given information on demographics and smoking history. They showed that this model could better determine an NLST participant’s benefit from CT lung screening. In particular, they showed that the 20% at least risk of lung cancer death in the NLST had only 1% of the prevented lung cancer deaths. In contrast, the 60% at highest risk had 88% of the prevented lung cancer deaths. These findings suggest that lung cancer death risk calculations could help to better determine a smoker’s benefits and harms from CT lung screening. Dr. Katki is working to make the lung cancer death risk assessment tool publicly available. He currently working on assessing the potential for using risk to better assess any smoker’s benefits and harms from undergoing CT lung screening and for risk-based management based on CT screening test results, based on the principle of “equal management of people at equal risk of cancer”. He also collaborates with Dr. Anil Chaturvedi on evaluating whether new biomarkers can provide more lung cancer death risk stratification and more CT lung screening “benefit stratification”, suggesting possible utility for lung screening.
Dr. Katki is interested in developing models for absolute risk estimation. He has helped develop methods and software for calculating absolute risk for general epidemiologic studies nested within enumerated cohorts, also known as “two-phase sampling”, which is in the R package NestedCohort. He has also worked to propose a hybrid risk regression model called “LEXPIT” that allows for both additive and multiplicative effects in logistic regression, which is coded in the R package blm.
Dr. Katki is interested all aspects of evaluating the potential of new biomarkers for clinical use. He has also proposed efficient study designs to assess the operating characteristics of new diagnostic tests when the current standard test has already been conducted on all specimens. He is interested in developing methods for evaluating the risk stratification afforded by a biomarker. He is also exploring the implications of the principle of “equal management of people at equal risk of disease” as a foundational principle for management.