As patients develop toxic side effects, cancer treatment is adapted over time by either delaying or reducing the dosage of the next chemotherapy cycle. Being at the same time risk factors for mortality and predictors of future exposure levels, toxicities represent time-dependent confounders for the effect of chemotherapy on patient survival. In the presence of confounders, classical survival approaches have limitations in causally interpreting the hazard ratio of the treatment, even when randomized. The Intention-To-Treat approach is widely used in chemotherapy studies, although it is far from representing everyday clinical practice. Marginal Structural Cox Models (Cox MSM) in combination with Inverse Probability of Treatment Weighting (IPTW) are a proper tool to evaluate the causal effects of an exposure on survival outcomes. In this work, using novel definitions of Received Dose Intensity and Multiple Overall Toxicity, suitable IPTW-based techniques and Cox MSM were designed to mimic a randomized trial where chemotherapy joint-exposure is no longer confounded by toxicities. In this pseudo-population, a crude analysis is sufficient to estimate the causal effect of joint-exposure modifications. This paper discusses an innovative and detailed analysis of complex chemotherapy data, with tutorial-like explanations about the difficulties encountered and the novel problem-solving strategies deployed. This work highlights the confounding nature of toxicities and shows the detrimental effect of not considering them in the analyses. To the best of our knowledge, this is the first study combining different methodologies in an innovative way to eliminate the toxicity-treatment-adjustment bias in chemotherapy trial data.

翻译：随着患者出现毒性副作用，癌症治疗会随时间调整，通过延迟或降低下一化疗周期的剂量。毒性反应既是死亡的风险因素，也是未来暴露水平的预测因子，因此构成了化疗对患者生存效应的时间依赖性混杂因素。在存在混杂因素的情况下，即使采用随机化设计，经典生存分析方法在因果解释治疗风险比方面仍存在局限性。意向性治疗分析法虽在化疗研究中广泛应用，但远不能代表日常临床实践。边际结构Cox模型结合逆概率治疗加权法是评估暴露对生存结局因果效应的合适工具。本研究采用新定义的“实际剂量强度”与“多重总体毒性”，设计了基于IPTW的适当技术与Cox MSM，以模拟化疗联合暴露不再受毒性混杂的随机试验。在此伪总体中，粗分析即可估计联合暴露修改的因果效应。本文对复杂化疗数据进行了创新性详细分析，以教程式讲解说明研究难点及所部署的新型问题解决策略。本研究揭示了毒性的混杂本质，并展示了不将其纳入分析的危害性。据我们所知，这是首项创新性整合多种方法以消除化疗试验数据中“毒性-治疗-调整”偏倚的研究。