Hormuzd A. Katki, Ph.D.
|Organization:||National Cancer InstituteDivision of Cancer Epidemiology & Genetics, Biostatistics Branch|
|Address:||NCI Shady GroveRoom 7E592|
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 for cancer screening and prevention. He is particularly interested in developing individualized risk-based approaches to cancer screening. His methodologic research focuses on estimating individual absolute risk, strategies for risk-based screening and management, and metrics for evaluating risk models and biomarkers. Dr. Katki is interested in mentoring both statisticians interested in cancer research, and also epidemiologists who want to use state-of-the-art quantitative and computational methods. If you are interested in a fellowship with Dr. Katki, please see our research training opportunities.
In spite of the definitive National Lung Screening Trial (NLST) and USPSTF guidelines recommending screening, CT lung-cancer screening is still not widespread. This is partly due to the inefficiency of screening. To make screening more efficient, Dr. Katki conducts research on using risk calculations to better identify those who benefit the most from lung screening and to propose risk-based management options during the course of screening.
Dr. Katki developed validated individualized risk models for lung cancer incidence (LCRAT: Lung Cancer Risk Assessment Tool) and mortality (LCDRAT: Lung Cancer Death Risk Assessment Tool). Using these models to select ever-smokers at highest risk should improve screening effectiveness and efficiency versus current USPSTF guidelines. To empower doctors and patients with risk information needed to decide about undergoing screening, Dr. Katki collaborates with Dr. William Klein to improve the NCI lung cancer screening risk tool, the Risk-based NLST Outcomes Tool (RNOT).
The R package lcmodels estimates risk from 9 published lung cancer models: LCRAT, LCDRAT, Bach, PLCOM2012, Spitz, Hoggart, LLP, LLPi, and Pittsburgh. The R package lcrisks provides the risk calculators that are used by RNOT.
Dr. Katki is conducting research on a Markov model for updating individual lung cancer risk with CT image findings during the course of screening. This model may be useful to extend screening intervals for those at sufficiently low risk of developing lung cancer.
Dr. Katki is interested in developing models for individualized risk estimation.
Dr. Katki has developed risk models for screening data, where some disease is already present at baseline (left-censored), some disease occurs between consecutive visits (interval censored), and some disease is unknown if it was prevalent or incident. These models, the logistic-Weibull and logistic-Cox models, can be accessed as part of R package PImixture. The models allow sampling weights.
He has helped develop methods and software for calculating absolute risk for case-cohort studies, or case-control studies nested within cohorts (also known as “two-phase sampling”) which is in the R package NestedCohort.
He has proposed a hybrid risk regression model called “LEXPIT” that allows for both additive and multiplicative effects in logistic regression, and allows sampling weights. LEXPIT is in the R package blm.
Dr. Katki is conducting research on improving the external validity of epidemiologic cohort analyses by including data from nationally representative surveys.
Dr. Katki is also helping with research to develop individualized models of years of life gained by screening to select people for screening. Years of life gained is a measure of the benefit of screening, and as such is more relevant than simply using risk to select people for screening.
Dr. Katki is interested all aspects of evaluating the potential of new biomarkers for clinical use.
In particular, he has done research quantify risk stratification, the ability of a test or model to separate those at high-risk from those at low-risk. His metric Mean Risk Stratification (MRS) is the average change in a person’s risk that is revealed by using a risk model or test. MRS better compares tests across populations with different disease prevalence by interpreting AUC in the context of prevalence. He has used MRS to compare the risk stratification from cervical screening tests and risk models to identify who in a family carries a mutation in BRCA1/2. The MRS webtool is part of the Biomarker Tools Suite.
Dr. Katki has developed methods for calculating diagnostic accuracy and agreement statistics under verification bias, when one test is conducted on only a subsample of specimens, in R package CompareTests.
Dr. Katki he led a team that calculated cervical cancer risks, using the logistic-Weibull model, for women 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 were published in a 2013 supplement of the Journal of Lower Genital Tract Disease .
He developed the “Risk Bar” for the risk-based App for the Consensus Guidelines for the Management of Abnormal Cervical Cancer Screening Tests and Cancer Precursors, based on woman’s history of HPV, Pap smear, and biopsy results.
Dr. Katki collaborates with Dr. Anil Chaturvedi on oral HPV and oropharyngeal cancer, conducting research on natural history with an eye towards future prevention.
Dr. Katki is developing risk-based approaches to help propose risk-based screening programs for genetic mutations, such as for BRCA1 and BRCA2.