HOPKINS PERSPECTIVE ON BIOSTATISTICS: A BRIEF HISTORY AND TODAY
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.
At Johns Hopkins Department of Biostatistics, research is characterized by a
commitment to statistical science, its foundations and methods, as well as the
application of statistical science to the solution of public health and
biomedical problems. As indicated in the two-way arrows in the figure below, 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, like that which occurs at Johns Hopkins.
Foundations
Methodology
Applications
Public Health and Biomedical Research
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 showed how case control studies can be used to draw valid inferences about parameters in prospective models. Richard Royall has led a transition in statistical reasoning from decision methods (p-values, tests of hypotheses) toward likelihood methods, which quantify scientific evidence. Charles Rohde, Kung-Yee Liang, Steve Goodman, Tom Louis, Constantine Frangakis, and others continue this tradition.
Research on statistical methodology has as its goal the creation of new tools for drawing inferences from data. To illustrate, Ron Brookmeyer and Mitch Gail developed the methodology used to monitor and project the size of the US AIDS epidemic; Kung-Yee Liang, Mei-Cheng Wang, and Scott Zeger developed methods for regression analysis with correlated responses. Daniel Scharfstein and Constantine Frangakis have developed techniques for assessing the possible impact of missing data in clinical trials and observational studies. Kung-Yee Liang, Rafael Irizarry, and Giovanni Parmigiani are developing new quantitative methods for genetics. Ingo Ruczinski has developed novel regression methods to predict how proteins will fold.
To accomplish societal goals, biostatistics and biostatisticians must research important substantive questions as well. For example, Francesca Dominici, Aidan McDermott, and Frank Curriero, and colleagues have used multiple national databases to determine the effects of air pollution on mortality across the 90 largest American cities. Marie Diener-West, Jim Tonascia, Steve Piantadosi, Steve Goodman, and others have led or collaborated in clinical trials of new therapeutic treatments. Karen Bandeen-Roche collaborates with gerontologists to determine the causes and course, and ultimately to postpone the onset, of disability in older adults. The Biostatistics Center faculty, including Richard Thompson, Carol Thompson, Elizabeth Johnson and Luu Pham, serve more than 150 biomedical clients each year.
Throughout its history and today, Hopkins Biostatistics has embraced a broad definition of our discipline, including foundations, methodology, and applications. The 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.