Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations. Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.
Dr. Raghunathan’s primary research interest is in developing methods for dealing with missing data in sample surveys and in epidemiological studies. The methods are motivated from a Bayesian perspective but with desirable frequency or repeated sampling properties. The analysis of incomplete data from practical sample surveys poses additional problems due to extensive stratification, clustering of units and unequal probabilities of selection. The model-based approach provides a framework to incorporate all the relevant sampling design features in dealing with unit and item nonresponse in sample surveys. There are important computational challenges in implementing these methods in practical surveys. He has developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data. Raghunathan’s other research interests include Bayesian methods, methods for small area estimation, combining information from multiple surveys, measurement error models, longitudinal data analysis, privacy, confidentiality and disclosure limitations and statistical methods for epidemiological studies. His applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences. Raghunathan is also involved in the Survey Methodology Program at the Institute for Social Research, a multidisciplinary team of sociologists, statisticians and psychologists, provides an opportunity to address methodological issues in: nonresponse, interviewer behavior and its impact on the results, response or measurement bias and errors, noncoverage, respondent cognition, privacy and confidentiality issues and data archiving. The Survey Methodology Program has a graduate program offering masters and doctoral degrees in survey methodology.
My current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. I also conduct research in statistical software.
Roderick Joseph Little, PhD, is the Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, Research Professor, Institute for Social Research, and Senior Fellow, Michigan Society of Fellows, at the University of Michigan, Ann Arbor.
Prof. Little’s primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.
Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.