Validating a Local Failure Risk Stratification for Use in Prospective Studies of Adjuvant Radiation Therapy for Bladder Cancer

Abstract

To inform prospective trials of adjuvant radiation therapy (adj-RT) for bladder cancer after radical cystectomy, a locoregional failure (LF) risk stratification was proposed. This stratification was developed and validated using surgical databases that may not reflect the outcomes expected in prospective trials. Our purpose was to assess sources of bias that may affect the stratification model's validity or alter the LF risk estimates for each subgroup: time bias due to evolving surgical techniques; trial accrual bias due to inclusion of patients who would be ineligible for adj-RT trials because of early disease progression, death, or loss to follow-up shortly after cystectomy; bias due to different statistical methods to estimate LF; and subgrouping bias due to different definitions of the LF subgroups. The LF risk stratification was developed using a single-institution cohort (n=442, 1990-2008) and the multi-institutional SWOG 8710 cohort (n=264, 1987-1998) treated with radical cystectomy with or without chemotherapy. We evaluated the sensitivity of the stratification to sources of bias using Fine-Gray regression and Kaplan-Meier analyses. Year of radical cystectomy was not associated with LF risk on univariate or multivariate analysis after controlling for risk group. By use of more stringent inclusion criteria, 26 SWOG patients (10%) and 60 patients from the single-institution cohort (14%) were excluded. Analysis of the remaining patients confirmed 3 subgroups with significantly different LF risks with 3-year rates of 7%, 17%, and 36%, respectively (P<.01), nearly identical to the rates without correcting for trial accrual bias. Kaplan-Meier techniques estimated higher subgroup LF rates than competing risk analysis. The subgroup definitions used in the NRG-GU001 adj-RT trial were validated. These sources of bias did not invalidate the LF risk stratification or substantially change the model's LF estimates.