Spreafico Marta, Ieva Francesca, Fiocco Marta
Mathematical Institute, Leiden University, Leiden, 2333 CC, The Netherlands.
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands.
BMC Med Res Methodol. 2024 Dec 3;24(1):296. doi: 10.1186/s12874-024-02416-x.
This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. Previous research has highlighted discrepancies between planned and actual RDI, even among patients randomized to the same treatment regimen. To mitigate toxic side effects, treatment adjustments, such as dose reduction or delayed courses, are necessary. Toxicities are therefore risk factors for mortality and predictors of future exposure levels. Toxicity introduces post-assignment confounding when assessing the causal effect of chemotherapy RDI on survival outcomes, a topic of ongoing debate.
Chemotherapy administration data from BO03 and BO06 Randomized Clinical Trials (RCTs) in ostosarcoma are employed to emulate a target trial with three RDI-based exposure strategies: 1) standard, 2) reduced, and 3) highly-reduced RDI. Investigations are conducted between subgroups of patients characterised by poor or good Histological Responses (HRe), i.e., the strongest known prognostic factor for survival in osteosarcoma. Inverse Probability of Treatment Weighting (IPTW) is first used to transform the original population into a pseudo-population which mimics the target randomized cohort. Then, a Marginal Structural Cox Model with effect modification is employed. Conditional Average Treatment Effects (CATEs) are ultimately measured as the difference between the Restricted Mean Survival Time of reduced/highly-reduced RDI strategy and the standard one. Confidence Intervals for CATEs are obtained using a novel IPTW-based bootstrap procedure.
Significant effect modifications based on HRe were found. Increasing RDI-reductions led to contrasting trends for poor and good responders: the higher the reduction, the better (worsen) was the survival in poor (good) reponders. Due to their intrinsic resistance to chemotherapy, poor reponders could benefit from reduced RDI, with an average gain of 10.2 and 15.4 months at 5-year for reduced and highly-reduced exposures, respectively.
This study introduces a novel approach to (i) comprehensively address the challenges related to the analysis of chemotherapy data, (ii) mitigate the toxicity-treatment-adjustment bias, and (iii) repurpose existing RCT data for retrospective analyses extending beyond the original trials' intended scopes.
本研究旨在采用一种新方法，分析降低骨肉瘤患者化疗治疗中接受剂量强度（RDI）对其生存的影响。先前的研究强调了计划的RDI与实际的RDI之间的差异，即使在随机分配到相同治疗方案的患者中也是如此。为了减轻毒性副作用，进行治疗调整，如剂量减少或疗程延迟是必要的。因此，毒性是死亡率的危险因素和未来暴露水平的预测指标。在评估化疗RDI对生存结果的因果效应时，毒性会引入分配后混杂因素，这是一个仍在争论的话题。
采用骨肉瘤BO03和BO06随机临床试验（RCT）的化疗给药数据，模拟一项具有三种基于RDI的暴露策略的目标试验：1）标准，2）降低，3）高度降低的RDI。在以组织学反应（HRe）差或好为特征的患者亚组之间进行调查，即骨肉瘤生存中已知最强的预后因素。首先使用治疗权重逆概率（IPTW）将原始人群转化为模仿目标随机队列的伪人群。然后，采用具有效应修正的边际结构Cox模型。最终将条件平均治疗效应（CATEs）测量为降低/高度降低的RDI策略与标准策略的受限平均生存时间之差。CATEs的置信区间使用基于IPTW的新型自举程序获得。
发现基于HRe的显著效应修正。RDI降低程度增加导致反应差和反应好的患者出现相反的趋势：降低程度越高，反应差（好）的患者生存率越高（越低）。由于对化疗具有内在抗性，反应差的患者可以从降低的RDI中获益，在5年时，降低暴露和高度降低暴露的平均获益分别为10.2个月和15.4个月。
本研究引入了一种新方法，（i）全面应对与化疗数据分析相关的挑战，（ii）减轻毒性 - 治疗调整偏差，（iii）重新利用现有RCT数据进行回顾性分析，其范围超出了原始试验的预期范围。
