9/29/2017

Post date: Oct 9, 2017 3:37:40 PM

Joint Seminar of HPC and SLAM Working Group

Title: Statistical Modeling of Human Fecundity with a view towards individualized risk prediction for pregnancy

Speaker: Rajeshwari Sundaram, Senior Investigator, Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Human fecundity, defined as the biologic capacity of men and women for reproduction irrespective of pregnancy intentions, is of considerable public health interest, given growing evidence supporting its worldwide decline. Some researchers argue that environmental reproductive and/or developmental toxicants are responsible for the decline while others posit that behavior is the culprit, given that couples are intentionally postponing childbearing. As clinical and public health groups work toward the implementation of preconception clinical guidance for couples at risk for pregnancy and more globally reproductive health, it is imperative to understand both male and female determinants of fecundity and fertility. To accomplish this goal, statistical models that incorporate biological features of both members of the couple are needed. This is challenging since complex multivariate longitudinal modeling techniques need to be developed to adequately reflect the biology. In this talk, I will focus on statistical modeling for predicting time-to-pregnancy and for understanding the effects of important behavioral factor, ie intercourse on time-to-pregnancy. I will discuss recently developed statistical models ranging from discrete survival time as well as models for day specific probabilities of pregnancy that have been developed to address the analytical challenges mentioned above while relaxing some implausible biological assumptions that existed in prior approaches. Lastly, I will discuss methods that allow us to assess some easily available longitudinal processes assessing behavior and menstrual cycle characteristics and show how they can be used to predict time-to-pregnancy dynamically. All these approaches will be illustrated on data arising through various prospective pregnancy study designs.