Abstract

P-value combination is an important statistical approach for information-aggregated decision making. It is foundational to a lot of applications such as meta-analysis, data integration, signal detection, and others. We propose two generic statistic families for combining p-values: gGOF, a general family of goodness-of-fit type statistics, and tFisher, a family of Fisher type p-value combination with a general weighting-and-truncation scheme. The two families unify many optimal statistics over a wide spectrum of signal patterns. Within these two families of statistics, data-adaptive omnibus tests are also designed for adapting the family-retained advantages to unknown signal patterns. For analyzing correlated data, we provide efficient solutions for analytical calculations of the p-value. We reveal the influence of data transformations to the signal-to-noise ratio and the statistical power under the Gaussian mean model and the generalized linear model. Applications of these methods are illustrated in gene-based SNP-set studies of genetic associations.

Speaker's Biography

Professor Zheyang Wu's research interest lies in applying the power of statistical science to promote biomedical researches. In statistical genetics, he is developing novel statistical theory and methodology to analyze genome-wide association (GWA) data and deep (re)sequencing data to hunt new genetic factors for complex human diseases. In epigenetics, he is studying gene expression regulation mechanisms through chromatin interaction, and RNA silencing pathways in the developmental stages of germ-line cells. In clinical studies, he is establishing statistical models to predict carotid atherosclerotic plaque and stroke based on time series longitudinal data and survival data.

Professor Wu received his PhD in Biostatistics at Yale University in 2009. He then joined the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), where he is currently a tenured Associate Professor.