Multitrial Evaluation of Progression-Free Survival as a Surrogate End Point for Overall Survival in First-Line Extensive-Stage Small-Cell Lung Cancer.

INTRODUCTION: We previously reported that progression-free survival (PFS) may be a candidate surrogate end point for overall survival (OS) in first-line extensive-stage small-cell lung cancer (ES-SCLC) using data from three randomized trials (Foster, Cancer 2011). In this validation study (N0424-Alliance), we assessed the patient-level and trial-level surrogacy of PFS using data from seven new first-line phase II/III ES-SCLC trials and across all 10 trials as well (seven new, three previous). METHODS: Individual patient data were utilized across the seven new trials (2259 patients) and all 10 trials (2855 patients). Patient-level surrogacy (Kendall's τ) was assessed using the Clayton copula bivariate survival model. Trial-level surrogacy was assessed through association of the log hazard ratios on OS and PFS across trials, including weighted (by trial size) least squares regression (WLS R²) of Cox model effects and correlation of the copula effects (copula R²). The minimum effect on the surrogate (MES) needed to detect a nonzero treatment effect on OS was also calculated. RESULTS: The median OS and PFS across all 10 trials were 9.8 and 5.9 months, respectively. PFS showed strong surrogacy within the 7 new trials (copula R² = 0.90 [standard error = 0.27], WLS R² = 0.83 [95% confidence interval: 0.43, 0.95]; MES = 0.67, and Kendall's τ = 0.58) and across all 10 trials (copula R² = 0.81 [standard errors = 0.25], WLS R² = 0.77 [95% confidence interval: 0.47-0.91], MES = 0.70, and Kendall's τ = 0.57). CONCLUSIONS: PFS demonstrated strong surrogacy for OS in first-line ES-SCLC based on this external validation study of individual patient data. PFS is a good alternative end point to OS and should be considered when resource constraints (time or patient) might make it useful or desirable in place of OS. Additional analyses are needed to assess its appropriateness for targeted agents in this disease setting.

Duke Scholars

Author Xiaofei Wang Biostatistics & Bioinformatics, Division of Biostatistics