DIRECTOR
Michael Hughes
Michael Hughes, PhD; Professor of Biostatistics and Director of the Statistical and Data Management Center for the AIDS Clinical Trials Group. His research involves a variety of issues concerning HIV, with particular emphasis on statistical methods for the design and analysis of HIV clinical trials. One area concerns methhods for the design and analysis of phase I/II studies for “special populations” such as infants, children and pregnant women. Such studies require novel dose-finding methods involving multiple outcome measures, including pharmacokinetic, anti-HIV activity and toxicity outcomes, and complexities related to long-term outcomes. More generally, this area extends to the design and analysis of bridging studies that allow translation of results from large clinical trials in one population to a second population (e.g. from the U.S. to sub-Saharan Africa). Another area of interest concerns the development of semi-parametric methods for longitudinal data analysis including informative missingness and censoring (as with repeated measurements of HIV RNA). These methods are critical for evaluating virologic and immunologic outcomes, as well as growth and development outcomes in HIV-infected children.
PRECEPTORS
Tianxi Cai
Tianxi Cai, ScD; Professor of Biostatistics. Her 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.
Victor DeGruttola
Victor DeGruttola, ScD; Professor of Biostatistics. His research activities focus on developments of statistical methods required for appropriate public health response to the AIDS epidemic. The aspects of the epidemic worked on includes modelling processes of infection, natural history of infection with HIV, and clinical research on AIDS therapies. Current research focusses on methods for relating HIV genetic mutations to resistance to antiretroviral drugs. Also serves as Director for the Adult Statistical & Data Analysis Center at the Harvard School of Public Health.
Wafaie Fawzi
Wafaie Fawzi, DPH; Professor of Nutrition and Epidemiology. Dr. Fawzi’ research focuses on examining the role of nutritional and other factors in the etiology of adverse health outcomes among populations in developing countries, with emphasis on infectious and perinatal outcomes among mothers and children. Dr. Fawzi and collaborators are implementing several large randomized controlled trials to examine the efficacy of various micronutrient supplements on the incidence and severity of a number of infectious diseases including pneumonia, diarrhea, tuberculosis, and HIV infection. In collaboration with colleagues at Muhimbili University in Dar es Salaam, Tanzania, the team completed a trial that documented a significant beneficial effect of periodic vitamin A supplementation on child mortality. In another large clinical trial prenatal multivitamin supplementation of HIV-infected women resulted in large and significant reductions in the risk of fetal loss, low birth weight, and severe prematurity. Currently, the group is examining whether the latter findings are generalizable to the larger population of HIV-negative women. As part of the HIV Prevention Trials Network at NIH the team is engaged in examining strategies for reducing perinatal and heterosexual transmission of infection.
Sebastien Haneuse
Sebastien Haneuse, PhD; Associate Professor of Biostatistics at HSPH. His methodologic research follows two general themes, the first of which focuses on the development of novel study designs that help address bias encountered in the analysis of data from observational studies. He looks to augment the data collection process with supplementary information that can then be used to directly address the various biases. The simultaneous development of statistical tools that ensure valid and efficient estimation and inference is a crucial aspect of this research. The second general theme of his research involves the development and use of flexible, so-called non- parametric, prior distributions for semi-parametric Bayesian analyses. Two key components of this research are (i) exploiting the flexibility of these specifications to gain additional insights into mechanisms and/or etiology, and (ii) overcoming the consequences of model misspecification, particularly in the analysis of correlated or longitudinal data.
Miguel Hernan
Miguel Hernan, PhD. Kolokotrones Professor of Biostatistics and Epidemiology. Dr. Hernan’s research is focused on methodology for causal inference, including comparative effectiveness of policy and clinical interventions. His team works to combine observational data, mostly untestable assumptions, and statistical methods to emulate hypothetical randomized experiments. They emphasize the need to formulate well defined causal questions, and use analytic approaches whose validity does not require assumptions that conflict with current subject-matter knowledge. For example, in settings in which experts suspect the presence of time-dependent confounders affected by prior treatment, we do not use adjustment methods (e.g., conventional regression analysis) that require the absence of such confounders. While causal inferences from observational data are always risky, an appropriate analysis of observational studies often results in the best available evidence for policy or clinical decision-making. At the very least, the findings from well designed and properly analyzed observational studies may guide the design of future randomized experiments. His applied work is focused on optimal use of antiretroviral therapy in persons infected with HIV, lifestyle and pharmacological interventions to reduce the incidence of cardiovascular disease, and the effects of erythropoiesis-stimulating agents among dialysis patients.
Michael Hughes
Michael Hughes, PhD; Professor of Biostatistics and Director of the Statistical and Data Management Center for the AIDS Clinical Trials Group. His research involves a variety of issues concerning HIV, with particular emphasis on statistical methods for the design and analysis of HIV clinical trials. One area concerns methhods for the design and analysis of phase I/II studies for “special populations” such as infants, children and pregnant women. Such studies require novel dose-finding methods involving multiple outcome measures, including pharmacokinetic, anti-HIV activity and toxicity outcomes, and complexities related to long-term outcomes. More generally, this area extends to the design and analysis of bridging studies that allow translation of results from large clinical trials in one population to a second population (e.g. from the U.S. to sub-Saharan Africa). Another area of interest concerns the development of semi-parametric methods for longitudinal data analysis including informative missingness and censoring (as with repeated measurements of HIV RNA). These methods are critical for evaluating virologic and immunologic outcomes, as well as growth and development outcomes in HIV-infected children.
Curtis Huttenhower, PhD; Professor of Computational Biology and Bioinformatics. Dr. Huttenhower’s research focuses on computational biology at the intersection of microbial community function and human health. The Huttenhower group works on a variety of computational methods for data mining in microbial communities, model organisms, pathogens, and the human genome. In practice, this entails a combination of computational methods development for mining and integrating large multi’omic data collections, as well as biological analyses and laboratory experiments to link the microbiome in human populations to specific microbiological mechanisms. The lab has worked extensively with the NIH Human Microbiome Project to help develop the first comprehensive map of the healthy Western adult microbiome, and it currently co-leads one of the “HMP2” Centers for Characterizing the Gut Microbial Ecosystem in Inflammatory Bowel Disease. This is one of many open problems in understanding how human-associated microbial communities can be used as a means of diagnosis or therapeutic intervention on the continuum between health and disease.
JP Onella, PhD; Associate Professor of Biostatistics. Dr. Onnela’s research involves two interrelated research themes. In statistical network science, the study of network representations of physical, biological, and social phenomena, his lab develops quantitative methods for studying social and biological networks and their connection to health. In digital phenotyping, or “the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, in particular smartphones,” he develops quantitative methods for studying social, behavioral, and cognitive phenotypes. The focus of the team in both statistical network science and digital phenotyping is development of new statistical and quantitative methods, but also co-leading or supporting several applied studies ranging from central nervous system disorders to women’s health. His group has developed and maintains the open source Beiwe research platform for high-throughput smartphone-based digital phenotyping.
Marcello Pagano
Marcello Pagano, PhD; Professor of Biostatistics. Dr. Pagano’s research interests include the epidemiology of HIV infection, especially biostatistical methods for the surveillance of the epidemic, and associated testing methodology. On surveillance methods he has concentrated on modeling the effects of reporting delay; on refining back-calculation methods, including research on how to overcome this method’s shortcoming when it comes to its inability to evaluate HIV infection in the recent (last 3 or 4 years) past; on how to handle doubly censored observations; longitudinal, observational studies; and economical methods for making the blood supply safer. Dr. Pagano plays an active role in the teaching program of the Department of Biostatistics having more than thirty years of teaching experience. He has been the primary thesis advisor for many students in addition to serving on many thesis research committees.
John Quackenbush, PhD; Henry Pickering Walcott Professor of Computational Biology and Bioinformatics. Dr. Quackenbush’s research group focuses on methods spanning the laboratory to the laptop that are designed to use genomic and computational approaches to reveal the underlying biology. In particular, he has been looking at patterns of gene expression in cancer with the goal of elucidating the networks and pathways that are fundamental in the development and progression of the disease.
James Robins
James Robins, MD; Professor of Epidemiology and Biostatistics. His research interests include causal inference for randomized and non-randomized HIV studies, longitudinal studies subject to nonrandom dropout, measurement error models, missing data problems, theoretical aspects of semiparametric models. Dr. Robins’ has a strong record of collaboration with students, postdoctoral fellows and other faculty on environmental health related problems. He has supervised several doctoral theses in Biostatistics, served on trainee research committees and supervised several summer projects.
Tyler VanderWeele
Tyler J. VanderWeele, PhD, John L. Loeb and Frances Lehman Loeb Professor of Epidemiology. Dr. VanderWeele’s methodological research is focused on theory and methods for distinguishing between association and causation in the biomedical and social sciences and, more recently, on psychosocial measurement theory. His empirical research spans psychiatric and social epidemiology; the science of happiness and flourishing; and the study of religion and health. He is the recipient of the 2017 Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS). He has published over three hundred papers in peer-reviewed journals; is author of the books Explanation in Causal Inference (2015), Modern Epidemiology (2021), and Measuring Well-Being (2021); and he also writes a monthly blog posting on topics related to human flourishing for Psychology Today.
Rui Wang, PhD, Associate Professor in the Dept. of Biostatistics. Dr. Wang’s research interests include the design, monitoring, and analysis of parallel and stepped-wedge cluster randomized trials, where a group of subjects, as opposed to individuals, are randomized to each of the treatment arms in the trial. The particular questions she is addressing include the investigation of how the complex correlation structure within clusters affects the sample size and power of the trial, and how to analyze data from such trials efficiently, taking into account the correlation structure and the issue of missing data. She has also been developing improved statistical techniques for a cross-sectional approach that, when combined with modern HIV screening methods, can substantially reduce the cost and increase the accuracy of HIV incidence estimation. Her research interests also include longitudinal modeling of non-linear trajectories and model selection, as well as addressing missing data issues in distributed data networks.
Lee-Jen Wei
Lee-Jen Wei, PhD; Professor of Biostatistics. His research is in the area of developing statistical methods for the design and analysis of clinical trials. He has developed numerous methods for analyzing data with multiple outcome or repeated measurements obtained from study subjects. In particular, his “multivariate Cox procedures” to handle multiple event times have become quite popular. His work with colleagues led to the development of alternative models to the Cox proportional hazards model for analyzing survival observations. He is also a senior statistician at the Statistical and Data Analysis Center, and works closely with the medical investigators in Pediatric AIDS clinical trials for evaluating new treatments for HIV patients.
Paige Williams
Paige Williams, PhD; Senior Lecturer on Biostatistics. Dr. Williams’ research is divided between development of statistical methods for AIDS clinical trials and environmental risk assessment. In the area of AIDS clinical trials, she has addressed statistical issues in the design, analysis, and sequential monitoring of trials conducted by the National AIDS Clinical Trial Group (ACTG) for prevention and treatment of opportunistic infections in HIV-infected patients. She was the Head of the Complications of HIV Section of Harvard’s Center for Biostatistics in AIDS Research (CBAR) from 1995-1997, and continues to serve as the senior statistician on a number of related clinical trials. Along with these collaborative activities, Dr. Williams has developed statistical methods for monitoring clinical trials with multiple survival endpoints, and has investigated the use of both CD4 and HIV viral load as predictors of the risk of opportunistic infections.