Thema: News: Weibull parameter model to survival analysis of glioma
News: Weibull parameter model to survival analysis of glioma
Tom[a]
01.07.2003 07:24:02
Zhonghua Liu Xing Bing Xue Za Zhi. 2003 Mar;24(3):229-32.
The application of Weibull parameter model to survival analysis of glioma
Jia HY, Wang JZ, Zhao JJ.
Second Hospital of Shandong University, Jinan 250033, China.
OBJECTIVE:
To investigate the factors affecting the survival and to predict the survival time of glioma.
METHODS:
Graph method was applied to fit the type of probability distribution of patients´ postoperative survival time. As one suitable model, Weibull regression model tested by residual analysis was used to identify prognostic factors for postoperative survival and to predict patients´ post-operative survival time.
RESULTS:
In multivariate analysis of Weibull regression model, following characteristics showed significant influence on postoperative survival time including: sex, age, epilepsy, type of glioma, density of glioma, type of surgery, times of surgery and mode of postoperative therapy. According to predictive value, patients were clustered into three groups. The survival probability of one year for the first group was less than 50% and that of five years for the second group about 50%, for the third group around 80%.
CONCLUSION:
Sex, age, epilepsy, type of glioma, density of glioma, type of surgery, times of surgery and mode of postoperative therapy were important risk factors and Weibull regression model might help to predict patients´ postoperative survival time.
The application of Weibull parameter model to survival analysis of glioma
Jia HY, Wang JZ, Zhao JJ.
Second Hospital of Shandong University, Jinan 250033, China.
OBJECTIVE:
To investigate the factors affecting the survival and to predict the survival time of glioma.
METHODS:
Graph method was applied to fit the type of probability distribution of patients´ postoperative survival time. As one suitable model, Weibull regression model tested by residual analysis was used to identify prognostic factors for postoperative survival and to predict patients´ post-operative survival time.
RESULTS:
In multivariate analysis of Weibull regression model, following characteristics showed significant influence on postoperative survival time including: sex, age, epilepsy, type of glioma, density of glioma, type of surgery, times of surgery and mode of postoperative therapy. According to predictive value, patients were clustered into three groups. The survival probability of one year for the first group was less than 50% and that of five years for the second group about 50%, for the third group around 80%.
CONCLUSION:
Sex, age, epilepsy, type of glioma, density of glioma, type of surgery, times of surgery and mode of postoperative therapy were important risk factors and Weibull regression model might help to predict patients´ postoperative survival time.
Tom[a]
