Place: 4274 Chamberlin Hall (Refreshments will be served)
Speaker: Jing Kong, UW Department of Statistics
Abstract: We present a method for examining mortality as it is seen to run in families, and lifestyle factors that are also seen to run in families, in a sub-population of the Beaver Dam Eye Study that has died by 2011. We observe that pairwise distance between death age in related persons is on average less than pairwise distance in death age between random pairs of unrelated persons. Our goal is to examine the hypothesis that pairwise di fferences in lifestyle factors correlate with the observed pairwise diff erences in death age that run in families. Szekely and coworkers have recently developed a method called distance correlation, that is suitable for this task with some enhancements relevant to the particular task at hand. We build a Smoothing Spline ANOVA (SS-ANOVA) model for predicting death age based on four major lifestyle factors generally known to be related to mortality and four of the major diseases contributing to mortality, to develop a lifestyle mortality risk vector and a disease mortality risk vector. We then examine to what extent pairwise diff erences in these scores correlate with the pairwise di fferences in mortality as they occur between family members and between unrelated persons. We fi nd signfi cant distance correlations between death ages, lifestyle factors, and family relationships. Considering only sib pairs compared to unrelated persons, distance correlation between siblings and mortality is, not surprisingly, stronger than that between more distantly related family members and mortality. The overall methodological approach here easily adapts to exploring relationships between multiple clusters of variables with observable (real-valued) attributes, and other factors for which only possibly nonmetric pairwise dissimilarities are observed.