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, 2018). 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.

Short Biography:

Dr. Paul Albert is senior investigator and chief of the Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute since 2016. Prior to joining the Division, Dr. Albert was senior investigator and chief of Biostatistics and Bioinformatics Branch in the Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development. He joined National Institutes of Health (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 National Cancer Institute. Dr. Albert received his Ph.D. in biostatistics from the Johns Hopkins University.

Dr. Albert's research interests primarily focus on complex modeling of correlated outcomes in biomedical sciences, including the analysis of longitudinal data, diagnostic testing, and data from biomarker studies. In addition, Dr. Albert's recent methodological research interests include the joint modeling of longitudinal and survival data, the analysis of high-dimensional chemical mixture models, and the analysis of circadian rhythms.

https://dceg.cancer.gov/about/staff-directory/albert-paul