Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding
Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding
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DOI：
10.1177/0962280220912524
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发表时间：
2020-03-30
影响因子：
2.3
通讯作者：
Siebert, U.
中科院分区：
文献类型：
--
作者：
Latimer, N. R.;White, I. R.;Siebert, U.
在肿瘤学试验中，对照组患者在随访期间经常切换到实验性治疗，通常是在疾病进展之后。在这种情况下，意向治疗分析将不会解决感兴趣的政策问题--与标准治疗相比，新治疗是否代表着对卫生保健资源的有效和成本效益的使用。保序结构失效时间模型(RPSFTM)、截尾权重逆概率模型(IPCW)和两阶段估计(TSE)经常被用来调整切换，以便为治疗补偿政策决策提供信息。使用一种简单的方法(TSEsimp)应用了TSE，假设疾病进展时间和转换时间之间没有时间依赖的混淆。如果进程和切换之间存在延迟，这是有问题的。本文介绍了TSEest，它使用结构嵌套模型和g-估计来解释时间依赖混杂，并与TSEsimp，RPSFTM和IPCW进行了比较。我们模拟了控制组患者可以在存在和不存在时间依赖性混淆的情况下切换到实验治疗的情景。我们改变了切换比例、治疗效果和审查比例。我们根据他们对控制组受限平均生存时间的估计来评估调整方法，在没有切换的情况下将观察到这些时间。所有方法在没有时间相关混淆的情况下都表现良好。TSEest和RPSFTM在具有时间依赖性混淆的场景中继续表现良好，但TSEsimp导致了实质性的偏差。IPCW在与时间相关的混杂情况下也表现良好，但在逆概率权重相对于受权群体的大小较高时除外，这种情况发生在适度样本量和高切换比例的组合情况下。TSEest是对可用于调整试验中的治疗切换以解决与政策相关的问题的方法集合的有用补充。
In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest - that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions.