PURPOSE: Post-therapy changes in prostate-specific antigen(PSA) have been proposed as surrogates for survival in clinical trials due to observed statistical associations. However, association alone does not satisfy the conditions of surrogacy. A measure that quantifies the amount of association is important. Using a population-based approach, we explore the relationship between PSA and survival and generate a measure to demonstrate the amount of the variation in survival explained by PSA. EXPERIMENTAL DESIGN: With serial PSA measurements from 254 patients with androgen-independent prostate cancer, we use a time-dependent Cox model with a nonlinear log relative risk function to quantify the strength of the association between time-dependent PSA and survival. The nonlinear log relative risk function provides a flexible Cox model and a more accurate measure of the strength of association between PSA and survival. RESULTS: Among these 254 patients, there were 247 deaths (median survival time = 13.0 months). Median follow-up time for those alive or censored was 59.3 months (range, 36.8-71.3 months). An association was observed between PSA and survival (P < 0.01, two-sided test). However, time-dependent PSA explains only 17% of the variation in survival. CONCLUSIONS: Use of this methodology demonstrates that there remains sufficient variation in survival unaccounted for by PSA measurements in this patient cohort. Other factors, perhaps unknown, exist that determine survival outcome. Consideration of PSA alone as a surrogate can produce misleading information regarding the risk of death; its use as a surrogate for survival is not warranted when designing a clinical trial in this patient population.
PURPOSE: Post-therapy changes in prostate-specific antigen(PSA) have been proposed as surrogates for survival in clinical trials due to observed statistical associations. However, association alone does not satisfy the conditions of surrogacy. A measure that quantifies the amount of association is important. Using a population-based approach, we explore the relationship between PSA and survival and generate a measure to demonstrate the amount of the variation in survival explained by PSA. EXPERIMENTAL DESIGN: With serial PSA measurements from 254 patients with androgen-independent prostate cancer, we use a time-dependent Cox model with a nonlinear log relative risk function to quantify the strength of the association between time-dependent PSA and survival. The nonlinear log relative risk function provides a flexible Cox model and a more accurate measure of the strength of association between PSA and survival. RESULTS: Among these 254 patients, there were 247 deaths (median survival time = 13.0 months). Median follow-up time for those alive or censored was 59.3 months (range, 36.8-71.3 months). An association was observed between PSA and survival (P < 0.01, two-sided test). However, time-dependent PSA explains only 17% of the variation in survival. CONCLUSIONS: Use of this methodology demonstrates that there remains sufficient variation in survival unaccounted for by PSA measurements in this patient cohort. Other factors, perhaps unknown, exist that determine survival outcome. Consideration of PSA alone as a surrogate can produce misleading information regarding the risk of death; its use as a surrogate for survival is not warranted when designing a clinical trial in this patient population.
Authors: Howard I Scher; Michael J Morris; William K Kelly; Lawrence H Schwartz; Glenn Heller Journal: Clin Cancer Res Date: 2005-07-15 Impact factor: 12.531
Authors: Sungmin Woo; Chong Hyun Suh; Andreas G Wibmer; Anton S Becker; Min Yuen Teo; Mithat Gönen; Hedvig Hricak; Howard I Scher; Michael J Morris; Hebert Alberto Vargas Journal: Clin Genitourin Cancer Date: 2021-11-15 Impact factor: 2.872
Authors: Geoffrey R Oxnard; Michael J Morris; F Stephen Hodi; Laurence H Baker; Mark G Kris; Alan P Venook; Lawrence H Schwartz Journal: J Natl Cancer Inst Date: 2012-08-27 Impact factor: 13.506
Authors: W Albrecht; H Van Poppel; S Horenblas; G Mickisch; A Horwich; V Serretta; G Casetta; J M Maréchal; W G Jones; S Kalman; R Sylvester Journal: Br J Cancer Date: 2004-01-12 Impact factor: 7.640
Authors: Wolfgang P Fendler; Svenja Reinhardt; Harun Ilhan; Andreas Delker; Guido Böning; Franz J Gildehaus; Christian Stief; Peter Bartenstein; Christian Gratzke; Sebastian Lehner; Axel Rominger Journal: Oncotarget Date: 2017-01-10