Longitudinal adverse event assessment in oncology clinical trials: the Toxicity over Time (ToxT) analysis of Alliance trials NCCTG N9741 and 979254

Abstract

BackgroundTraditional methods of reporting adverse events (AEs) in clinical trials are inadequate for modern oncology therapies with chronic administration. Conventional analysis and display of maximum grade AEs do not capture toxicity profiles that evolve over time or longer lasting, lower grade toxicity as does this longitudinal Toxicity over Time (ToxT) approach.MethodsGraphical and analytical routines were compiled into an automated and standardized format to comprehensively analyze AEs. Plots visualizing summary statistics or individual patient data over discreet time points were combined with statistical methodology including longitudinal techniques (repeated measures models that describe the changes in AEs over each time period; time-to-event analyses of first, worst, or high grade; and area under the curve (AUC) analyses summarizing AE profiles over the entire study). The analytic capability of ToxT was demonstrated using two completed North Central Cancer Treatment Group (NCCTG)/Alliance clinical trials in cancer therapy (N9741, NCT00003594) and symptom control (979254).FindingsBar charts and stream plots showed higher incidences of dry mouth occurring late in 979254 for venlafaxine compared to placebo (week 1 [baseline]: 13% vs 22%, p=0.20; week 5: 49% vs 2%, p<0.0001) and increased nausea early for IROX vs FOLFOX in N9741 (cycle 1: mean grade 1.1 versus 0.6, p<0.0001). Event charts visually depicted earlier occurrences of higher diarrhea grades for IROX patients and the AUC analysis indicated a higher magnitude of diarrhea experience over time in IROX compared to FOLFOX (4.2 versus 2.9, p<0.0001).InterpretationThe ToxT analytic approach incorporates the dimension of time and offers a more comprehensive depiction of toxicity than current methods. With new, continuously administered targeted agents and maintenance regimens, these improved longitudinal analyses are directly relevant to patients and are imperative in oncology clinical trials.FundingUS National Cancer Institute Alliance NCORP Research Base Grant (UG1CA 189823) and Mayo Comprehensive Cancer center Grant–Biostatistics (P30CA 15083).