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Background Taxane-induced peripheral neuropathy (TIPN) is a dose-limiting adverse effect. Ganglioside-monosialic acid (GM1) functions as a neuroprotective factor. We assessed the effects of GM1 on the prevention of TIPN in breast cancer patients. Methods We conducted a randomized, double-blind, placebo-controlled trial including 206 patients with early-stage breast cancer planning to receive taxane-based adjuvant chemotherapy with a follow-up of more than 1 year. Subjects were randomly assigned to receive GM1 (80 mg, day −1 to day 2) or placebo. The primary endpoint was the Functional Assessment of Cancer Treatment Neurotoxicity subscale score after four cycles of chemotherapy. Secondary endpoints included neurotoxicity evaluated by National Cancer Institute Common Terminology Criteria for Adverse Events Version 4.0 and the Eastern Cooperative Oncology Group neuropathy scale. All statistical tests were two-sided. Results In 183 evaluable patients, the GM1 group reported better mean Functional Assessment of Cancer Treatment Neurotoxicity subscale scores than patients in the placebo group after four cycles of chemotherapy (43.27, 95% confidence interval [CI] = 43.05 to 43.49 vs 34.34, 95% CI = 33.78 to 34.89; mean difference = 8.96, 95% CI = 8.38 to 9.54, P < .001). Grade 1 or higher peripheral neurotoxicity in Common Terminology Criteria for Adverse Events v4.0 scale was statistically significantly lower in the GM1 group (14.3% vs 100.0%, P < .001). Additionally, the GM1 group had a statistically significantly lower incidence of grade 1 or higher neurotoxicity assessed by Eastern Cooperative Oncology Group neuropathy scale sensory neuropathy (26.4% vs 97.8%, P < .001) and motor neuropathy subscales (20.9% vs 81.5%, P < .001). Conclusions The treatment with GM1 resulted in a reduction in the severity and incidence of TIPN after four cycles of taxane-containing chemotherapy in patients with breast cancer.

Background In clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data. Methods The main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package ‘CutpointsOEHR’ has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed. Results Simulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p -value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models. Conclusions In general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models. Electronic supplementary material The online version of this article (10.1186/s12874-019-0738-4) contains supplementary material, which is available to authorized users.

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