Biostatistics is a statistical discipline focusing on theory and application of statistical methods for the analysis of problems related to biology, medicine and public health. The ultimate intent of Biostatistics is to better understand the factors that affect human health through judicious use of statistical methods.
The colossal advances in computational methods in the last couple of decades transformed Bayesian Inference from somewhat of a conceptual statistical paradigm based on philosophical consistency to an integral statistical tool for analyzing today’s highly complex data. Powerful computational tools allow Bayesian methods to tackle large and complex statistical problems with relative ease where frequentist methods can only approximate or fail altogether. Our faculty are involved in developing modern Bayesian methods for change point analysis, clinical trial designs, correlated time series processes, functional data analysis, genetics and genomics, network meta analysis.
Big Data refers to data that are not only “Big” in their size, but also too complex to be analyzed using standard analytic tools – they are indeed challenging in many ways. Biomedical data from research laboratories that use advanced biomedical technology such as microarray, next generation sequencing and DNA Methylation Beadchips are some examples of “big” biomedical data. Our faculty conduct research of Big Biomedical Data modeling, with specializations in (i) Computational Genomics by using computational tools to analyze large scale high-throughput genomic data; (ii) Epigenomics by developing methods to understand cell modifications that are indicative of complex diseases; (iii) Population Genetics by using DNA sequences sampled from populations to study population size changes, migration and admixture; (iv) Association and Linkage by developing new statistical methods and computational algorithms to study the genetic basis of diseases; and (v) Data Mining where exploratory data analysis is performed for generating biological/medical hypotheses.
Change point analysis is a collection of statistical methods concerning the changes in the distributions or in the parameters of a distribution that describes the underlying model of any data naturally ordered either in time or in position. It has a wide spectrum of applications in industrial quality management, climatology, economics and finance, medicine, genetics, etc. Our faculty have specialty in Bayesian Change Point Detection and Parametric Change Point Analysis with applications to genomic data.
Generalized linear models are widely used in various applications and they embrace a large class of statistical models. In this research area, our faculty are actively involved in research of cluster analysis of continuous and categorical outcomes, modeling longitudinal data of multiple outcomes that change over time simultaneously, and mixed effect models by modeling repeated measures over time where the response is continuous, nominal, or ordinal.
Nonparametric statistical inference is a branch of statistics in which no assumption about the form of distribution of the population under consideration is made. Because of unknown population distribution, classical method will not work as they should be and hence may lead to wrong results. Resampling techniques such as permutation tests, cross-validation, jackknife and bootstrap can be incorporated in developing efficient and practical nonparametric procedures, when parametric methods may not be feasible. Robust methods are extension of nonparametric procedures in the sense that it deviates from parametric form of the population with small deviation, but still produces reliable and relatively efficient estimates and test procedures.
Translational research is about translating innovative and advanced results from basic sciences research to new diagnosis and treatment regiments for the enhancement of human health and wellbeing. Our faculty are actively involved in developing and expanding methods used in translational research, which include biomarker data analysis, clinical trials, latent variable modeling, messy data analysis and survival analysis, among others.
Epidemiology is the study of the frequency, distribution, and determinants of disease and other health outcomes in populations. Epidemiologic studies are frequently conducted to describe the health status of populations, explain the etiology of a disease or health condition, predict the occurrence of disease, or effectively control its distribution in order to prevent the onset of new cases.
Clinical epidemiology refers to the application of epidemiology in a clinical setting. Specifically, epidemiologic tools are used to promote evidence-based practices, increased efficiency, and risk factor determination in hospital or clinical environments. Our faculty have a rich history of experience and collaboration in cancer epidemiology, epidemiology of aging, psychiatric epidemiology and outcomes research.
Communities, whether local or abroad, are a big determinant of an individual’s health and well-being. In our department, our faculty focuses on improving the health and wellness of communities at local and global levels. Specifically, we have research experience in reducing health disparities, improving rural health and conducting program evaluations in support of evidence-based practices.
Epidemiological modeling is a widely used set of techniques that seek to determine factors associated with health outcomes, as well as study scenarios that can be used in order to explore disease impact and control strategies. Our faculty are experienced in a variety of modeling techniques associated with infectious disease epidemiology and mathematical epidemiology, specifically including latent variable modeling, outbreak investigations and disease simulations.
The foundation of genetic epidemiology is to unravel the genetic basis of complex diseases such as cancer and diabetes, which involve multiple genes, environmental factors and their interactions. Many of our faculty conduct research in developing new statistical methods and computational algorithms to study the genetic basis of these diseases. Specific areas of research include admixture mapping, associations and linkage, behavioral genetics, evolution, and population genetics.
Spatial epidemiology involves the description and analysis of disease based on geographic variations. Spatial epidemiology has existed since the beginning of modern epidemiology when John Snow, the “Father of Modern Epidemiology”, mapped cases of cholera deaths during the mid-19th century cholera outbreak in London. Through Dr. Snow’s seminal work of mapping cholera cases, he revealed that the cholera outbreak was resultant from waste contaminated drinking water, and thus debunking the common era theory of “miasmas” (poisonous gas from sewage causing extreme sickness). Modern spatial epidemiology includes disease mapping, geographic correlation and clustering based on statistical modeling. Geographic information systems (GIS), a computer system for capturing, storing, querying, and analyzing geospatial data; is commonly utilized to perform spatial epidemiologic analysis. Our faculty are spatial epidemiology experts and have produced nationally acclaimed work using spatial epidemiologic methodology.
The One Health philosophy is a transdisciplinary approach through which organizations promote cooperation and collaboration among human, animal and ecosystem health professionals. Because many infectious diseases of humans have zoonotic origins, the importance of merging the human, animal and environmental health disciplines is certainly apparent. In this research area, our faculty are experienced in local and global capacity building, disease surveillance and risk determination methods.