Kaplan-Meier Method in Tumor Doubling Time Estimation

• Activity Number: 548
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Yufeng Li*+ and Choo Hyung Lee and Donald Buchsbaum
• Companies: University of Alabama At Birmingham and UAB and UAB
• Keywords: Tumor doubling time ; Gompertz curve ; exponential distribution ; survival function ; log-rank test

Abstract

The assessment of tumor response to treatment is a typical task of experimental cancer research. Usually tumor growth delay data in vivo consist of at least two groups of animals: control and treatment. Tumor Doubling Time (TDT) is widely used for quantification of tumor growth rate for prognostic purposes and can quantify therapeutic effects of different treatment modalities. Some studies assume that the growth of an unperturbed tumor follows the Gompertz curve, and the TDT is determined from two volume estimations with measurement time intervals using the first derivative of the Gompertz model; others assume that tumor growth follows an exponential function and TDT is estimated with a nonlinear function. However, in the case of the Xenograft Tumor Model, neither models fit the tumor growth with different treatment strategy, so that the TDT will have a biased estimation. Thus we investigate using model free Kaplan-Meier estimates in TDT estimation. The simulation results show that the KM method in TDT estimation is more robust than other model based methods. The KM method can also provide a comparison between treatments in TDT simultaneously.