【摘要】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.

【报告人简介】 Paul S. Albert, PhD, Chief and Senior Investigator, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. He received his Ph.D. in biostatistics from the Johns Hopkins University in 1988. His 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. He also develops new methodological techniques for predicting future disease progression or poor outcomes from longitudinally collected biomarkers, including Markov modeling techniques for recurrent events with misclassification. He is currently the Associate Editors of Biometrics, Statistics in Medicine, Fertility and Sterility etc .He was awarded ASA Fellow, NIH Merit Awards, NICHD Director’s Award etc. He has published 340 more papers.