Project 1 People
Brent Coull -- Project Leader email@example.com Brent Coull is a Professor Biostatistics in the Department of Biostatistics and the Department of Environmental Health at Harvard School of Public Health. Dr. Coull's current research interests fall into the broad areas of categorical data analysis and semiparametric regression modeling. Recent topics in the analysis of categorical data include capture-recapture mixture models, random effect models for multiple discrete binary outcomes, confidence intervals for a binomial proportion, and order-restricted methods for stratified contingency tables. In the area of semiparametric regression modeling, he has focused on the development of such models for complex data structures often encountered in public health settings, such as cross-over and longitudinal settings
Francesca Dominici -- Professor and Senior Associate Dean of Research (co-PI) firstname.lastname@example.org Francesca Dominici is a Professof of Biostatistics in the Department of Biostatistics and Senior Associate Dean in the Office of the Dean at Harvard School of Public Health. Her main research focuses on developing statistical methods for integrating and analyzing large and heterogeneous datasets to evaluate the health impacts of new discoveries and interventions. She is committed to: 1) advancing statistics by disseminating new methodology that better accounts for confounding and model misspecification in drawing inferences from large observational studies; and 2) applying the newly developed methodology to large and complex databases to address critical questions in public health and to impact policy.
Xihong Lin -- Professor (PI) email@example.com Xihong Lin is Professor of Biostatistics and Co-ordinating Director of Program in Quantitative Genomics of Harvard School of Public Health. Her group's major research interests lie in development and application of statistical and computational methods for anlaysis of high-dimensional genomic and 'omics data in population and clinical sciences, and for analysis of correlatd data, such as longitudinal, clustered and spatial data. The group is interested in statistical genetics and genomics, genetic and epigenetic epidemiology, genes and environment and medica l genomics. Current research projects include genome-wide association studies, next generation sequencing studies, gene-envir onment interactions, and genome-wide DNA methylation studies, pathway analysis and network analysis, proteomics.
Tianxi Cai -- Professor firstname.lastname@example.org Professor Cai is a Professor of Biostatistics in the Department of Biostatistics at Harvard School of Public Health. Dr. Cai's current research interests are mainly in the area of biomarker evaluation; model selection and validation; prediction methods; personalized medicine in disease diagnosis, prognosis and treatment; statistical inference with high dimensional data; and survival analysis. In addition to her methdological research, Dr. Cai also collaborates with the I2B2 (Informatics for Integrating Biology and the Bedside) center on developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases.
Articles: PubMed, Google Scholar.
Jarvis Chen -- Research Scientist email@example.com Jarvis Chen is a Social Epidemiologist and Research Scientist in the Department of Society, Human Development, and Health at Harvard School of Public Health. His research focuses on racial/ethnic, socioeconomic, and geographic disparities in health. His methodological interests include multilevel and spatiotemporal modelling techniques, methods for handling missing data, and latent variable models.
Sebastien Haneuse -- Assistant Professor firstname.lastname@example.org Sebastien Haneuse joined the Department of Biostatistics as an Assistant Professor of Biostatistics in October of 2010. The focus of his research is the development of novel study designs to address bias encountered in observational studies. To a greater extent, methods development in the statistical sciences focuses on post-hoc analysis techniques that attempt to overcome deficiencies in the data collection process. In many instances, however, there may still be insufficient information in the observed data to inform the analysis technique. Examples include inferring individual-level associations from ecological data and the adjustment for selection bias; without additional information one must resort to making empirically unverifiable assumptions. His research looks to augment the data collection process with information that can then be used to directly address the various biases.
Nancy Krieger -- Professor email@example.com Nancy Krieger is Professor of Society, Human Development, and Health at the Harvard School of Public Health and Co-Director of the HSPH Interdisciplinary Concentration on Women, Gender, and Health. Author of Epidemiology and The People's Health: Theory and Context (Oxford University Press, 2011), Dr. Krieger is an internationally recognized social epidemiologist who received her PhD in Epidemiology in 1989 from the University of California at Berkeley, and in 2004 she became one of the ISI highly cited scientists, a group comprising “less than one-half of one percent of all publishing researchers.” Dr. Krieger’s work addresses three topics: (1) conceptual frameworks to understand, analyze, and improve population health and reduce health inequities, including the ecosocial theory of disease distribution she has been developing since 1994; (2) etiologic research on societal determinants of population health and health inequities; and (3) methodologic research on improving monitoring of health inequities. Examples of her epidemiologic research include: studies on racism, discrimination and health, including blood pressure and birth outcomes; socioeconomic and racial/ethnic disparities in breast cancer; and research on appropriate measures of social class (individual, household, and neighborhood), both for population-based monitoring of social inequalities in health and studying women, gender, class, and health.
Kyu Ha Lee firstname.lastname@example.org Dr. Lee is currently a Research Associate in the Department of Biostatistics. He is developing Bayesian statistical models to analyze cancer data under the supervision of Dr. Francesca Dominici and Dr. Brent Coull. His research interests include: Bayesian survival analysis, variable selection, and their applications in high dimensional analysis.
Deborah Schrag -- Professor email@example.com Dr. Schrag is a Professor of Medicine at Harvard Medical School and the Chief of the Division of Population Sciences in the Department of Medical Oncology at the Dana-Farber Cancer Institute. Dr. Schrag is a health services researcher focusing on the study of cancer care delivery. She describes the patterns and outcomes of cancer treatment in order to determine how well treatments with efficacy established in the clinical trial setting are translated into practice in non-research settings. This involves strategic use of observational and found data sources and application of statistical techniques to evaluate the impact of treatment interventions in non-experimental settings. Recent work has focussed on technology diffusion and efforts to determine what determines how rapidly new treatments are adopted and the factors that drive utilization. Her current project seeks to evaluate the quality of care delivered to indigent patients with cancer who are insured by the State Medicaid programs in New York and California. By using Medicaid enrollment and claims histories linked to other data sources including hospital discharge abstracts and tumor registry data, the goal is to prioritize areas for improving care delivery to the poor. Ultimately, the goal is to inform design of sustainable systems architecture for ongoing surveillance of the quality of cancer care.
Tyler VanderWeele -- Professor firstname.lastname@example.org Tyler VanderWeele is a Professor of Epidemiology in the Department of Epidemiology and the Department of Biostatistics at Harvard School of Public Health. His methodologic research concerns how we distinguish between association and causation in the biomedical and social sciences and the study of the mechanisms by which causal effects arise. The current focus of his work includes the analysis of pathways, assessments of interaction, and the evaluation of network and spillover effects in which one person’s exposure will affect the outcomes of another. His research employs counterfactual theory and ideas from causal inference to clarify and formalize concepts used by epidemiologists, biomedical researchers and social scientists. His empirical work has been in the areas of perinatal, psychiatric and genetic epidemiology; various fields within the social sciences; and the study of religion and health. In perinatal epidemiology, he has worked on evaluating prenatal care indices, on the analysis of trends in birth outcomes, and on assessing the role of preterm birth in mediating the effects of prenatal exposures on mortality outcomes. In genetic epidemiology, I have been studying gene-environment interaction and the pathways by which genetic variants operate. In psychiatric epidemiology, I have been studying the feedback and inter-relationships between depression, loneliness and subjective well-being. His work in the social sciences has included the study of educational interventions, micro-finance programs, social network effects, and judicial decisions. His work in religion and health is oriented towards assessing the mechanisms by which religion and spirituality affect health outcomes.
Cory Zigler email@example.com Cory is an Assistant Professor of Bioatstistics in the Department of Biostatistics. His primary research focus is the development of Bayesian causal inference methods for problems in public health and biomedical research. Recently, he has been developing methods for assessing the public health impact of air quality regulations and for advancing Bayesian methods that use propensity scores for comparing the effectiveness of clinical treatments using large administrative claims data.