Biography

Hormuzd A. Katki received a B.S. in Math from the University of Chicago then an M.S. in Statistics from Carnegie-Mellon University. Dr. Katki's NIH career began in 1995 in the former Division of Computer Research and Technology working on optimal design of Nuclear Magnetic Resonance experiments. He joined NCI in 1999 as a Staff Scientist and was appointed as a Principal Investigator in 2009. 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.

Research Interests

Models to estimate absolute risks of diseases or of carrying mutations

For my Ph.D. thesis, I improved a clinically-used statistical model (BRCAPRO) that uses family history of breast/ovarian cancer to predict who in a family carries a BRCA1/2 mutation to help decide about offering genetic testing to patients. To identify the issues of greatest clinical importance, I independently sought out and attended weekly clinical meetings about whether to offer BRCA1/2 testing to patients. I chose my three thesis topics (effects of misreported family history, accounting for medical interventions, accounting for non-breast/ovarian cancers) as the most clinically-important and feasible improvements based on my experience at the clinical meetings. My improvements have been integrated into BRCAPRO and are in clinical use. Most importantly, accounting for non-breast/ovarian cancers fixed the outstanding over-prediction bias in BRCAPRO.

I am leveraging the experience gained during my thesis to an ambitious and important project: constructing a clinical risk-prediction model for cervical precancer. Current clinical algorithms are not yet poised to take advantage the flood of information provided by HPV tests, vaccination, and next-generation biomarkers. A risk calculator should incorporate HPV vaccination, HPV test results, Pap smears, and other clinically-available biomarkers, to categorize women into risk-based management groups. If fully successful, this risk calculator will enable future guidelines for clinical management to be based on risk. I have articulated this vision and am laying the groundwork for this project. I lead a multidisciplinary NIH-wide team to advise me on the multiple epidemiologic, clinical, statistical, and risk communication challenges. I plan to produce an initial model to help shape clinical practice and later provide successive refinements.



Efficient sampling and plans to improve epidemiologic study design

To save resources in cohort studies while retaining statistical efficiency, the exposures are measured on most disease cases but only a well-chosen sample of the controls. However, since exposures are not measured on all cohort members, standard methods cannot conduct survival analyses to estimate Kaplan-Meier survival curves or fit Cox models to estimate hazard ratios. Analyzing such studies solely as case-control studies ignores the information in the controls missing exposure measurements but still having information on outcomes and other confounder variables. Such studies are better analyzed as two-phase designs which extract information from all cohort members; subsets of this design include the case-cohort and nested case-control designs, but we allow for general stratified sampling as well. Our methods can estimate hazard ratios, survival curves and attributable risks for general studies nested within cohorts. Our methods can realize impressive information gains by using the entire cohort, and permit efficient sampling designs for controls to have exposure measurements. Finally, our methods can extract information from surrogates for exposure observed on the full cohort. My R package NestedCohort provides software for these methods.

In three collaborations, we proposed comparing a new diagnostic test to a pre-existing test already conducted on all specimens, by conducting the new test on only a judicious subsample of specimens. I introduced methods to estimate agreement statistics and conduct symmetry tests when one test is conducted on only a subsample. These methods achieve adequate statistical efficiency while greatly reducing study costs and specimen consumption. I am currently working on efficient study designs that use my methods for comparing diagnostic tests. Methods to compare diagnostic tests can also be applied to compare risk prediction models by grouping risks into categories. I plan to apply my methods to quantify the improvement in lung cancer risk prediction by measuring circulating C-reactive protein levels.



Unmeasured host risk factors and their role in etiology and prevention

Unmeasured host risk factors can have observable impact on disease risk. For example, since the vast majority of genetic mutations predisposing breast cancer risk remain unknown, women who test negative for their families mutation in BRCA1/2 may remain at above-average cancer risk if they have a family history of cancer beyond that accounted for by their family's mutation and other known risk factors. We addressed this controversy by proposing a novel metric to quantify residual familial risk due to unknown host risk factors and showed that the additional risk could justify continued, or even increased, breast cancer screening.

It has long been acknowledged that women respond differently to HPV infection and vaccination. These differences between women ("frailty") are likely due to undiscovered risk factors, including unknown host immune mechanisms. I am working on quantifying the role that unknown host factors play in the epidemiology of multiple HPV infections, persistence of HPV infection, and in measures of response to HPV vaccination, and in estimating the population-level impact such unknown factors could play in response to infection and vaccination.



Keywords

Absolute risk, attributable risk, BRCA1/2, BRCAPRO, Human Papillomavirus, growth curves, two-phase design, case-cohort design, nested case-control design, verification bias

Software

• CompareTests
• NestedCohort

Selected Publications

• Katki HA, Sanders C, Graubard BI, Bergen A. Using DNA fingerprints to infer familial relationships within NHANES III households. Journal of the American Statistical Association, In Press.
• Chaturvedi AK, Caporaso NE, Katki HA , Wong HL, Chatterjee N, Pine SR , Chanock SJ , Goedert JJ, Engels EA. C-reactive protein and risk of lung cancer. Journal of Clinical Oncology, In Press.
• Katki HA, Wacholder S, Solomon D, Castle PE, Schiffman M. Keynote Comment -- Risk estimation for the next generation of prevention programmes for cervical cancer. Lancet Oncology, 2009; 10(11): 1022-3.
• Katki HA, Blackford A, Chen S, and Parmigiani G. Multiple Diseases in Carrier Probability Estimation: Accounting for Surviving All Cancers Other than Breast and Ovary in BRCAPRO. Statistics in Medicine, 2008; 27(22): 4532-4548.
• Katki HA. Invited Commentary: Evidence-based Evaluation of p-values and Bayes Factors. American Journal of Epidemiology,, 2008; 168(4): 384-388.
• Katki HA, Mark SD. Survival Analysis for Cohorts with Missing Covariate Information. R News, 2008; 8(1): 14-19.
• Katki HA, Gail MH, Greene MH. Keynote Comment - Breast-cancer risk in BRCA-Mutation-Negative Women from BRCA-Mutation-Positive Families, Lancet Oncology, 2007; 8(12): 1042-1043.
• Katki HA. Incorporating Medical Interventions into Carrier Probability Estimation for Genetic Counseling. BMC Medical Genetics, 2007; Mar 22, 8:13
• Mark SD and Katki HA. Specifying and Implementing Nonparametric and Semiparametric Survival Estimators in Two-Stage (sampled) Cohort Studies with Missing Case Data. Journal of the American Statistical Association , 2006; 101(474):460-471
• Katki HA, Effect of Misreported Family History on Mendelian Mutation Prediction Models. Biometrics , 2006; 62(2):478-487
• Katki HA, Engels EA, and Rosenberg PS. Assessing Uncertainty in Reference Intervals via Tolerance Intervals: Application to a Mixed Model Describing HIV Infection. Statistics in Medicine, 2005; 24(20):3185-3198.
• Engels EA, Katki HA, Nielsen NM, Winther JF, Hjalgrim H, Gjerris F, Rosenberg PS, Frisch M, Cancer incidence in Denmark following exposure to poliovirus vaccine contaminated with simian virus 40. Journal of the National Cancer Institute, 2003; 95:532-539.
• Katki, H., Weiss, G.H., Keifer, J.E., Taitelbaum, H., Spencer, R.G.S. Optimization of Magnetization Transfer Experiments to Measure First-Order Rate Constants and Spin-Lattice Relaxation Times. NMR in Biomedicine, 1996; 9:135-139.

Collaborators

DCEG Collaborators

• Philip Castle, Ph.D.; Nilanjan Chatterjee, Ph.D.; Anil Chaturvedi, Ph.D.; Melissa Freisen, Ph.D.; Mitchell Gail, M.D, Ph.D; Arpita Ghosh, Ph.D.; Mark Greene, M.D; Barry Graubard, Ph.D; Allan Hildesheim, Ph.D.; Aimee Kreimer, Ph.D.; Philip Rosenberg, Ph.D; Mark Roth, M.D.; Mahboobeh Safaien, Ph.D.; Mark Schiffman, M.D.; Rachael Stolzenberg-Solomon, Ph.D.; Sholom Wacholder, Ph.D; Nicolas Wentzensen, M.D., Ph.D.; Regina Ziegler, Ph.D.

Other NCI Collaborators

• Sean Altekruse, Ph.D.
• Sharon Pine, Ph.D.
• John Schiller, Ph.D.
• Diane Solomon, M.D.

Other Scientific Collaborators

• Andrew Bergen, Ph.D., Stanford Research International, CA
• Sining Chen, Ph.D, University of Medicine and Dentistry of New Jersey
• Patti Gravitt, Ph.D., Johns Hopkins University, MD
• Esther John, Ph.D., Northern California Cancer Center
• Maura Gillison, Ph.D., Ohio State University, Columbus, OH
• Elena Martinez, Ph.D., Arizona Cancer Center
• Giovanni Parmigiani, Ph.D, Dana-Farber Cancer Institute, Boston, MA
• Jeffrey Roberts, Ph.D., Food and Drug Administration, MD
• Chris Sanders, Ph.D., MedCo Health, MD
• Rajeshwari Sundaram, Ph.D., Eunice Kennedy Shriver National Institute of Child Health and Human Development
• Ravi Varadhan, Ph.D., Johns Hopkins University, Baltimore, MD
• Sophia Wang, Ph.D., City of Hope, Duarte, CA