Shanshan Zhao

Biography

Shanshan Zhao, Ph.D., joined the NIEHS Biostatistics and Computational Biology Branch as a principal investigator in January 2015, and holds a secondary appointment in the Epidemiology Branch. Her main research interest is in risk assessment and risk prediction, and her research areas include survival data analysis, biomarker evaluation, mediation analysis, measurement error methodologies, epigenetics and cancer research. Zhao is focusing on statistical methods for analyzing multivariate failure time data, to facilitate risk assessment of multiple diseases. Although the analysis of univariate failure time has been well developed, there are still big gaps in multivariate failure time methodology. In practice with rare diseases, we may not be able to observe the disease of interest in all study participants (i.e., these subjects are censored). However, we might observe other events during the study. If the observed events are related to the main study outcome, we could use the data to fill in information about the main study outcome on those censored participants. For example, a study to predict time to stroke can benefit from information on time to coronary heart disease, given that these two outcomes are related. In addition, we might be interested in understanding how times to different diseases are related to each other, and how disease onset times relate within families, and whether the relatedness depends on some risk factors. Zhao works on semiparametric models for bivariate failure times to address these issues. Zhao is interested in developing risk prediction models for breast cancer. It is well known that family history is an important risk factor for breast cancer. However, the risk of family history differs by number of first-degree relatives with breast cancer, total number of first-degree relatives, current and diagnosis ages of first-degree relatives and current age of the woman. Zhao is developing statistical models for better using family history data, based on the NIEHS Sister Study data, which enrolled over 50,000 women with at least one sister with breast cancer.

Research Interest

Biostatistics & Computational Biology