The importance of adjusting for treatment crossover bias in oncology clinical trials: An analysis of the EGF104900 trial in metastatic breast cancer (mBC).

Abstract

636 Background: EGF104900 is a phase III trial in mBC that compared lapatinib + trastuzumab (LAPTRZ, n=146) with LAP monotherapy (LAP, n=145). The trial allowed LAP subjects to switch to LAPTRZ on documented disease progression (following ≥4 weeks of LAP). Conventional intent-to-treat (ITT) analysis does not control for potential bias due to treatment crossover. The Rank Preserving Structural Failure Time Model (RPSFTM) estimates event times had patients not switched, and performs re-censoring. The approach preserves randomization and ordering of patients’ observed survival times, assuming that patients switching benefit from the treatment effect seen in those initially randomized to the treatment arm. The method is recognized by NICE, UK, as an appropriate method for adjusting for crossover bias. Methods: 53% of LAP subjects crossed over to LAPTRZ. Analyses were stratified by hormone receptor status and visceral/non-visceral disease. RPSFTM was implemented in Stata (using White’s strbee procedure). Absolute overall survival (OS) was estimated using a parametric survival distribution fitted to the trial data with/without crossover adjustment. The unadjusted ITT Cox hazard ratio (HR) was compared with the crossover adjusted RPSFTM estimate, and the absolute gain in OS evaluated. Results: Median OS in the LAPTRZ and LAP arms was 61 and 41 weeks, respectively (ITT HR 0.74, 95% CI: 0.56, 0.96); RPSFTM crossover-adjusted median OS for LAP was 32 weeks (RPSFTM HR 0.52; 95% CI: 0.29, 0.92). Mean OS for LAPTRZ was estimated under a Weibull distribution as 74 weeks, compared with 57 (ITT) and 44 (RPSFTM) weeks for LAP. Treatment with LAPTRZ was estimated to extend mean OS by 30 weeks controlling for treatment crossover using RPSFTM compared with 17 weeks by ITT. Conclusions: Oncology trials are often subject to treatment crossover. Controlling for potential treatment crossover bias can result in greater estimates of gain in OS compared with ITT analysis. In the context of mBC such differences are of great importance to patients, clinicians, and healthcare payers. Treatment crossover-analyses are therefore also important for estimating cost-effectiveness in oncology.