|
Activity Number:
|
488
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 7, 2008 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biopharmaceutical Section
|
| Abstract - #300708 |
|
Title:
|
Covariate-Adjusted Nonparametric Survival Curve Estimation
|
|
Author(s):
|
Honghua Jiang*+ and Jame Symanowski and Yongming Qu and Yanping Wang
|
|
Companies:
|
Eli Lilly and Company and Nevada Cancer Institute and Eli Lilly and Company and Eli Lilly and Company
|
|
Address:
|
, , ,
|
|
Keywords:
|
Survival curve ; non-parametric ; adjusting for covariates
|
|
Abstract:
|
Kaplan-Meier survival curve estimation is a commonly used non-parametric method to evaluate survival distributions for groups of patients in the clinical trial setting. However, this method does not permit covariate adjustment which reduces bias and may increase precision. Tangen and Koch in 1999 introduced a nonparametric covariate-adjusted method to estimate survival rates for certain given time intervals, relying only on the assumption that there is no association between covariates and treatment groups in a randomized clinical trial. We extended this nonparametric covariate-adjusted method to develop a new model to estimate the survival rates for treatment groups at any time point when an event occurs. Simulation studies are conducted to investigate the model performance. This model is illustrated with an oncology clinical trial example.
|
