Stat4Onc 2019 Symposium Short Courses
April 25, 2019 Morning courses 8:30 AM to 12:00 noon with one break
Afternoon courses 1:30 PM to 5:00 PM with one break
Course 1 (Morning Course) Survival Analysis Methods for Non-Proportional Hazards
Instructor – Professor Lu Tian, Stanford University
In a prospective clinical study to compare two groups, the primary end point is often the time to a specific event (for example, disease progression, death). The hazard ratio estimate is routinely used to empirically quantify the between-group difference under the assumption that the ratio of the two hazard functions is approximately constant over time. When this assumption is plausible, such a ratio estimate may capture the relative difference between two survival curves. However, the clinical meaning of such a ratio estimate is difficult, if not impossible, to interpret when the underlying proportional hazards assumption is violated. In this course, we will discuss several critical concerns regarding this conventional practice and propose an attractive alternative for quantifying the underlying differences between groups based on restricted mean survival time (RMST). I will discuss various issues in employing RMST in practical analysis including statistical inference, result interpretation, selecting the truncation point, study design, power comparison, regression adjustment and extensions to competing risk and recurrent events settings. We will discuss the pros and cons of the RMST-based analysis and demonstrate that it is competitive to its hazard ratio-based conventional counterparts in many real world applications.
Dr. Tian is Professor at the Department of Biomedical Data Science of Stanford University. Lu Tian received his Sc.D. in Biostatistics from Harvard University. He has considerable experience in statistical methodological research, planning large epidemiological studies, performing data management for randomized clinical trials and conducting applied data analysis. His current research interest includes developing statistical methods in survival analysis, semiparametric regression modelling, high-dimensional data analysis, precision medicine and meta-analysis. He has published more than 200 peer reviewed journal articles and currently served as the Associate Editor of Chance, Biometrics and Statistics in Medicine.