Our Unique Perspective to Biostatistics

Biostatistics comprises the reasoning and methods for using data as evidence to address public health and biomedical questions. It is an approach and a set of tools for designing studies and for quantifying the resulting evidence, for quantifying what we believe, and for making decisions.

Biostatistics research here at the Johns Hopkins Bloomberg School of Public Health is characterized by a commitment to statistical science, its foundations and methods, as well as the application of statistical and data science to the solution of public health and biomedical problems. The two-way arrows in the figure below indicate that research on foundations, methods, and applications is mutually supportive. To be excellent, biostatistical research must be built on a foundation of first-rate public health and biomedical research, such as that which occurs at Johns Hopkins.

Research on foundations has as its goal the development of better strategies, or ways of reasoning, for empirical research. For example, past chair William Cochran demonstrated how observational studies can be used to draw inferences about the causal effect of a treatment on a health outcome. Jerry Cornfield, the fourth Department chair, showed how case control studies can be used to draw valid inferences about parameters in prospective models. Richard Royall led a transition in statistical reasoning from decision methods (p-values, tests of hypotheses) toward likelihood methods, which quantify scientific evidence. More recently, Betsy Ogburn developed foundational approaches to study causal effects in the presence of interference, and Constantine Frangakis has developed a framework for handling post-exposure factors, known as principal stratification. 

Research on statistical methodology has as its goal the creation of new tools for drawing inferences from data. To illustrate, Scott Zeger, together with Mei-Cheng Wang and former faculty member Kung-Yee Liang, developed methods for regression analysis with correlated responses. Michael Rosenblum has pioneered adaptive designs by which to more efficiently and effectively conduct clinical trial research. Hongkai Ji has led in the development of methods to delineate gene transcription pathways at both the bulk and single cell scales. Brian Caffo, Ciprian Crainiceanu, Martin Lindquist, Vadim Zipunnikov and their colleagues are advancing methods to interpret data of massive scope as arise in neurological images, accelerometers, and other advanced research technologies. Jessie Tong develops approaches for efficient learning across study sites when individual data can’t be shared, known as federated learning. Faculty such as Nilanjan Chatterjee and Elizabeth Stuart also work on approaches for integrating data from multiple sources for prediction or causal purposes. Faculty in the Department also embrace, aim to better understand, and develop AI approaches, including work by Yiqun Chen on the use of AI in genomics and genetics research, and how AI can be used in evidence synthesis.

Biostatistics also includes research on important substantive questions. Martin Lindquist leads large-scale studies to better measure pain using brain imaging data. Marie Diener-West, Jim Tonascia, and others have led or collaborated in clinical trials of new therapeutic treatments. Nilanjan Chatterjee is creating polygenic scores by which to identify persons at risk for developing major diseases. Ingo Ruczinski and his colleagues are identifying genetic determinants of cancers, autism, cleft lip, and other diseases. Karen Bandeen-Roche leads in programs, educational initiatives and research to determine the causes and course, and ultimately to postpone the onset, of disability and frailty in older adults.

Throughout its history, the Department of Biostatistics has embraced a broad definition of our discipline, including foundations, methodology, and applications. Our faculty's commitment to this inclusive perspective and the support of the School's administration and faculty are two of the intangible yet critical components of the Department's current and future success.