Survival analysis is a collection of statistical methods used to address questions that have to do with whether and when an event of interest takes place. It is “the analysis of data that correspond to the time from a well-defined time origin until the occurrence of some particular event or end-point (Collett, 1994).” In this workshop, participants will learn fundamental concepts and skills to conduct survival analysis, and know how to apply these techniques to social, behavioral, and health research. The topics covered by this workshop include types of censoring mechanisms, descriptive methods for survival data (i.e., the Kaplan-Meier method and comparison of survival functions between groups), the discrete-time models, the piecewise exponential model, the Cox proportional hazards model, multivariate analysis of autocorrelated time-to-event data, and statistical power analysis pertaining to survival modeling.
Shenyang Guo, Ph.D., is Frank J Bruno Distinguished Professor at Brown School of Washington University in St Louis, the Yangtze River Scholar at Xi’an Jiaotong University, and the fellow of American Academy of Social Work and Social Welfare. He is an expert on the application of advanced statistical models to the solution of social welfare problems. Guo is the author (with Mark Fraser) of Propensity Score Analysis: Statistical Methods and Applications, Second Edition (2015), a comprehensive guide to the many ways that propensity scores can be used to improve causal inference. Other books include Survival Analysis (2010) and Structural Equation Modeling (2011) (with Natasha Bowen). He has published more than 80 journal articles and book chapters. He is on the editorial boards of Social Service Review, Journal of the Society for Social Work and Research, and Children and Youth Services Review. He teaches graduate courses on survival analysis, hierarchical linear modeling, growth curve modeling, structural equation modeling, and propensity score analysis.