Analyzing and effectively communicating the efficacy and toxicity of treatment is the basis of risk benefit analysis (RBA). More efficient and objective tools are needed. We apply Chauhan Weighted Trajectory Analysis (CWTA) to perform RBA with superior objectivity, power, and clarity. We used CWTA to perform 1000-fold simulations of RCTs using ordinal endpoints for both treatment efficacy and toxicity. RCTs were simulated with 1:1 allocation at defined sample sizes and hazard ratios. We studied the simplest case of 3 levels each of toxicity and efficacy and the general case of the advanced cancer trial, with efficacy graded by five RECIST 1.1 health statuses and toxicity by the six-point CTCAE scale (6 x 5 matrix). The latter model was applied to a real-world dose escalation phase I trial in advanced cancer. Simulations in both the 3 x 3 and the 6 x 5 advanced cancer matrix confirmed that drugs with both superior efficacy and toxicity profiles synergize for greater statistical power with CWTA-RBA. The CWTA-RBA 6 x 5 matrix reduced sample size requirements over CWTA efficacy-only analysis. Application to the dose finding phase I clinical trial provided objective, statistically significant validation for the selected dose. CWTA-RBA, by incorporating both drug efficacy and toxicity, provides a single test statistic and plot that analyzes and effectively communicates therapeutic risks and benefits. CWTA-RBA requires fewer patients than CWTA efficacy-only analysis when the experimental drug is both more effective and less toxic. CWTA-RBA facilitates the objective and efficient assessment of new therapies throughout the drug development pathway. Furthermore, several advantages over competing tests in communicating risk-benefit will assist regulatory review, clinical adoption, and understanding of therapeutic risks and benefits by clinicians and patients alike.

翻译：分析与有效传达治疗的疗效和毒性是风险-效益分析的基础。当前需要更高效、更客观的分析工具。本研究应用Chauhan加权轨迹分析，以更高的客观性、统计效能和清晰度进行风险-效益分析。我们采用CWTA对使用有序终点评估疗效和毒性的随机对照试验进行了1000次模拟。试验模拟采用1:1分配，设定特定样本量和风险比。我们研究了毒性（3级）与疗效（3级）的最简情况，以及晚期癌症试验的通用情况——其中疗效按RECIST 1.1的5种健康状况分级，毒性按6级CTCAE量表评估（构成6×5矩阵）。后一模型应用于真实世界的晚期癌症剂量递增I期试验。在3×3矩阵和6×5晚期癌症矩阵中的模拟均证实：当药物同时具有更优疗效和更低毒性时，CWTA-RBA能产生协同增强的统计效能。与仅分析疗效的CWTA相比，CWTA-RBA的6×5矩阵可降低样本量需求。在剂量探索I期临床试验中的应用，为所选剂量提供了客观且具有统计学显著性的验证。CWTA-RBA通过整合药物疗效与毒性数据，生成单一检验统计量和可视化图表，有效分析与传达治疗风险与获益。当实验药物兼具更高疗效和更低毒性时，CWTA-RBA比仅分析疗效的CWTA需要更少的患者样本。CWTA-RBA有助于在药物研发全过程中客观高效地评估新疗法。此外，其在风险-效益传达方面相较于竞争性检验方法的若干优势，将辅助监管审查、临床采纳，并促进临床医生与患者对治疗风险与获益的理解。