Al Tawil Amani, McGrath Sean, Ristl Robin, Mansmann Ulrich
Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, Munich, Germany.
Pettenkofer School of Public Health, Faculty of Medicine, Ludwig-Maximilians-Universität München, Elisabeth-Winterhalter-Weg 6, 81377, Munich, Germany.
BMC Med Res Methodol. 2024 Dec 20;24(1):314. doi: 10.1186/s12874-024-02437-6.
Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the presence of treatment switching. Consequently, decision makers may instead be interested in causal effects of hypothetical treatment strategies that do not allow for treatment switching. For example, the phase 3 ALTA-1L trial showed that brigatinib may have improved Overall Survival (OS) compared to crizotinib if treatment switching had not occurred. Their sensitivity analysis using Inverse Probability of Censoring Weights (IPCW), reported a Hazard Ratio (HR) of 0.50 (95% CI, 0.28-0.87), while their initial ITT analysis estimated an HR of 0.81 (0.53-1.22).
We used a directed acyclic graph to depict the clinical setting of the ALTA-1L trial in the presence of treatment switching, illustrating the concept of treatment-confounder feedback and highlighting the need for g-methods. In a re-analysis of the ALTA-1L trial data, we used IPCW and the parametric g-formula to adjust for baseline and time-varying covariates to estimate the effect of two hypothetical treatment strategies on OS: "always treat with brigatinib" versus "always treat with crizotinib". We conducted various sensitivity analyses using different model specifications and weight truncation approaches.
Applying the IPCW approach in a series of sensitivity analyses yielded Cumulative HRs (cHRs) ranging between 0.38 (0.12, 0.98) and 0.73 (0.45,1.22) and Risk Ratios (RRs) ranging between 0.52 (0.32, 0.98) and 0.79 (0.54,1.17). Applying the parametric g-formula resulted in cHRs ranging between 0.61 (0.38,0.91) and 0.72 (0.43,1.07) and RRs ranging between 0.71 (0.48,0.94) and 0.79 (0.54,1.05).
Our results consistently indicated that our estimated ITT effect estimate (cHR: 0.82 (0.51,1.22) may have underestimated brigatinib's benefit by around 10-45 percentage points (using IPCW) and 10-20 percentage points (using the parametric g-formula) across a wide range of model choices. Our analyses underscore the importance of performing sensitivity analyses, as the result from a single analysis could potentially stand as an outlier in a whole range of sensitivity analyses.
Clinicaltrials.gov Identifier: NCT02737501 on April 14, 2016.
随机临床试验中的治疗转换给进行因果推断带来了挑战。意向性分析（ITT）在存在治疗转换的情况下往往无法充分捕捉治疗的因果效应。因此，决策者可能更关注不允许治疗转换的假设治疗策略的因果效应。例如，3期ALTA - 1L试验表明，如果未发生治疗转换，与克唑替尼相比，布加替尼可能改善总生存期（OS）。他们使用删失逆概率加权法（IPCW）进行的敏感性分析报告的风险比（HR）为0.50（95%置信区间，0.28 - 0.87），而他们最初的ITT分析估计的HR为0.81（0.53 - 1.22）。
我们使用有向无环图来描绘存在治疗转换时ALTA - 1L试验的临床情况，阐明治疗 - 混杂因素反馈的概念，并强调对g方法的需求。在对ALTA - 1L试验数据的重新分析中，我们使用IPCW和参数化g公式来调整基线和随时间变化的协变量，以估计两种假设治疗策略对OS的影响：“始终使用布加替尼治疗”与“始终使用克唑替尼治疗”。我们使用不同的模型规格和权重截断方法进行了各种敏感性分析。
在一系列敏感性分析中应用IPCW方法得出的累积风险比（cHRs）范围在0.38（0.12，0.98）至0.73（0.45，1.22）之间，风险比（RRs）范围在0.52（0.32，0.98）至0.79（0.54，1.17）之间。应用参数化g公式得出的cHRs范围在0.61（0.38，0.91）至0.72（0.43，1.07）之间，RRs范围在0.71（0.48，0.94）至0.79（0.54，1.05）之间。
我们的结果一致表明，我们估计的ITT效应估计值（cHR：0.82（0.51，1.22））可能在广泛的模型选择中低估了布加替尼的益处约10 - 45个百分点（使用IPCW）和10 - 20个百分点（使用参数化g公式）。我们的分析强调了进行敏感性分析的重要性，因为单次分析的结果在一系列敏感性分析中可能会成为异常值。
Clinicaltrials.gov标识符：2016年4月14日的NCT02737501。
