Department of Statistics
Presents the
Robert W. Makuch Distinguished
Lecture in Biostatistics
Featuring
Paul S. Albert, Ph.D.
Chief and Senior Investigator
Biostatistics Branch
Division of Cancer Epidemiology and Genetics
National Cancer Institute
Innovative Approaches to the Statistical Analysis of Circadian Rhythm Data:
Uncovering the Patterns of life
ABSTRACT
Circadian rhythms are defined as a biological endogenous process that repeats at an approximate 24-hour period. Increasingly these processes are recognized in their importance in understanding disease processes. In 2017, for example, the Nobel prize for physiology was given for discoveries of molecular mechanisms controlling these rhythms. This talk will focus on our recent work on the statistical modeling of longitudinally collected circadian rhythm data. I will begin with a discussion of a shape invariant model for Gaussian data that can be easily be fit with standard software (Albert and Hunsberger, Biometrics, 2005). This model was subsequently extended for modeling longitudinal count data (Ogbagaber et al., Journal of Circadian Rhythms, 2012). More recently we developed a statistical model for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a population of individuals (Kim and Albert, Journal of the American Statistical Association, in press). We develop a latent variable Poisson modeling approach with both circadian and stochastic short-term trend (autoregressive latent process) components that allow for individual variation in the degree of each component. A parameterization is proposed for modeling covariate dependence on the proportion of these two model components across individuals. In addition, we incorporate covariate dependence in the overall mean, the magnitude of the trend, and the phase-shift of the circadian pattern. Innovative Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed models are considered and compared using the deviance information criterion. We illustrate this methodology with longitudinal physical activity count data measured in a longitudinal cohort of adolescents. Lastly, I will describe our recent methodological work focusing on examining the circadian rhythms of metabolites in a controlled environment.
A majority of this work is joint with Dr. Sungduk Kim at the NCI.
DATE: Wednesday, April 4, 2018
TIME: 4:00 p.m.
PLACE: Philip E. Austin Building – Room 108
Coffee will be served at 3:30 p.m. in the Noether Lounge (AUST 326)
Paul S. Albert was appointed senior investigator and chief of the Biostatistics Branch, Division of Cancer Epidemiology & Genetics in 2016. Prior to joining the Division, Dr. Albert was senior investigator and chief of Biostatistics and Bioinformatics Branch in the Division of Epidemiology, Statistics, and Prevention in the Eunice Kennedy Shriver National Institute of Child Health and Human Development. He came to the NIH in 1998, first as a staff fellow in the National Institute of Neurological Disorders and Stroke in the Biometry and Field Studies Branch, later as a mathematical statistician in the National Heart Lung and Blood Institute, and the Division of Cancer Treatment and Diagnosis. Dr. Albert received his Ph.D. in biostatistics from the Johns Hopkins University.
Robert Makuch is a professor in the Department of Biostatistics at the Yale School of Public Health and Director of the Regulatory Affairs Track. A graduate of the University of Connecticut (BA), University of Washington (MA – mathematics), and Yale University (MPhil, PhD), Professor Makuch worked at the National Cancer Institute (NCI) and the World Health Organization’s International Agency for Research on Cancer early in his career. He also was heavily involved in HIV research from the mid 80's through the early-mid 90's. He participated on the data monitoring committee for the original AZT vs. placebo randomized clinical trial in AIDS patients, and served on numerous committees for the NCI and the National Institute of Allergy and Infectious Diseases. He also worked closely with the Food and Drug Administration (FDA), developing and implementing more than 100 HIV studies. He also served as a Special Government Employee (SGE) to the FDA. He returned to Yale in 1986, and has worked extensively on methodologic issues in clinical trials and large population-based studies since. Another area of interest involves detection of rare adverse drug events, especially in the post-marketing environment. These areas of methodologic research evolved as a result of his continued interest (since the mid 80s) in regulatory affairs science. In addition, Makuch developed a regulatory affairs track at YSPH for its students, and over the past 6 years has been the leader of numerous training programs for senior delegations of the Chinese Food and Drug Agency. His areas of medical application include cancer, HIV, arthritis, and cardiovascular disease.
In 2003, Makuch received the American Statistical Association Fellow Award for his numerous contributions to the field. In 2008, Makuch was received a Distinguished Alumni Award from the University of Connecticut. In 2012, Makuch was nominated to serve on the University of Connecticut Dean's Advisory Board for the College of Liberal Arts and Sciences. He also developed a 5-year biostatistics training program in Japan, in collaboration with the Japanese government. His primary research interests continue to be methodologic issues in the design, conduct, and analysis of clinical and large-population/epidemiologic studies. Design and sample size considerations for Phase IV studies is another active research area, in which a new class of hybrid designs has been proposed for scientific and regulatory purposes to detect rare adverse events.
For more information, contact: Tracy Burke at tracy.burke@uconn.edu