A Krongrad1, H Lai, S Lai. 1. Department of Urology, and Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, Florida, USA.
Abstract
PURPOSE: Traditional survival analytical tools do not reveal the ability of significant prognostic factors to predict (that is, explain variation in) survival. We used survival data in patients with prostate cancer to illustrate how the association of factors with survival diverges from their ability to explain variation in survival; bladder cancer was included as a point of general comparison. MATERIALS AND METHODS: We used the 1973 to 1990 records of the Surveillance Epidemiology and End Results program. Multivariate proportional hazards models were used to identify factors that significantly associated with survival. The proportion of variation explained by these factors was estimated with the Schemper method. RESULTS: The dataset included 10,636 patients with prostate cancer and 1,070 with bladder cancer. Median survival was significantly longer in prostate than bladder cancer; other characteristics were similarly distributed. Age, stage and marital status were associated with survival in both cancers (p value range 0.0001 to 0.0009). The total proportion of variation explained was 7.1% and 32.1% for prostate and bladder cancer, respectively. In prostate cancer, age, stage and marital status explained 0.6, 5.5 and 0.4%, of the adjusted proportion of variation explained, respectively, and in bladder cancer, they explained 14.7, 8.9 and 0.6%, respectively. CONCLUSIONS: Proportional hazards models identified but did not reveal the ability of significant prognostic factors to explain variations in survival. The proportion of variation explained analyses illustrate why predicting survival is so difficult, especially in prostate cancer. The prognostic factors used do not possess the ability to explain variation in survival; new prognostic factors must be identified.
PURPOSE: Traditional survival analytical tools do not reveal the ability of significant prognostic factors to predict (that is, explain variation in) survival. We used survival data in patients with prostate cancer to illustrate how the association of factors with survival diverges from their ability to explain variation in survival; bladder cancer was included as a point of general comparison. MATERIALS AND METHODS: We used the 1973 to 1990 records of the Surveillance Epidemiology and End Results program. Multivariate proportional hazards models were used to identify factors that significantly associated with survival. The proportion of variation explained by these factors was estimated with the Schemper method. RESULTS: The dataset included 10,636 patients with prostate cancer and 1,070 with bladder cancer. Median survival was significantly longer in prostate than bladder cancer; other characteristics were similarly distributed. Age, stage and marital status were associated with survival in both cancers (p value range 0.0001 to 0.0009). The total proportion of variation explained was 7.1% and 32.1% for prostate and bladder cancer, respectively. In prostate cancer, age, stage and marital status explained 0.6, 5.5 and 0.4%, of the adjusted proportion of variation explained, respectively, and in bladder cancer, they explained 14.7, 8.9 and 0.6%, respectively. CONCLUSIONS: Proportional hazards models identified but did not reveal the ability of significant prognostic factors to explain variations in survival. The proportion of variation explained analyses illustrate why predicting survival is so difficult, especially in prostate cancer. The prognostic factors used do not possess the ability to explain variation in survival; new prognostic factors must be identified.
Authors: Daniel N Costa; B Nicolas Bloch; David F Yao; Martin G Sanda; Long Ngo; Elizabeth M Genega; Ivan Pedrosa; William C DeWolf; Neil M Rofsky Journal: Magn Reson Imaging Date: 2013-04-18 Impact factor: 2.546