The primary analysis of randomized screening trials for cancer typically adheres to the intention-to-screen principle, measuring cancer-specific mortality reductions between screening and control arms. These mortality reductions result from a combination of the screening regimen, screening technology and the effect of the early, screening-induced, treatment. This motivates addressing these different aspects separately. Here we are interested in the causal effect of early versus delayed treatments on cancer mortality among the screening-detectable subgroup, which under certain assumptions is estimable from conventional randomized screening trial using instrumental variable type methods. To define the causal effect of interest, we formulate a simplified structural multi-state model for screening trials, based on a hypothetical intervention trial where screening detected individuals would be randomized into early versus delayed treatments. The cancer-specific mortality reductions after screening detection are quantified by a cause-specific hazard ratio. For this, we propose two estimators, based on an estimating equation and a likelihood expression. The methods extend existing instrumental variable methods for time-to-event and competing risks outcomes to time-dependent intermediate variables. Using the multi-state model as the basis of a data generating mechanism, we investigate the performance of the new estimators through simulation studies. In addition, we illustrate the proposed method in the context of CT screening for lung cancer using the US National Lung Screening Trial data.

癌症随机筛查试验的主要分析通常遵循意向筛查原则，测量筛查组和对照组之间癌症特异性死亡率的降低。这些死亡率降低是筛查方案、筛查技术和早期筛查诱导治疗的效果相结合的结果。这促使分别解决这些不同的方面。在这里，我们感兴趣的是早期治疗与延迟治疗对筛查可检测亚组癌症死亡率的因果影响，在某些假设下，可以从使用工具变量类型方法的常规随机筛查试验中估计。为了定义感兴趣的因果效应，我们为筛选试验制定了一个简化的结构多状态模型，基于假设的干预试验，其中筛查检测到的个体将被随机分为早期治疗和延迟治疗。筛查检测后癌症特异性死亡率的降低通过病因特异性风险比进行量化。为此，我们基于估计方程和似然表达式提出了两个估计量。这些方法将现有的用于事件发生时间和竞争风险结果的工具变量方法扩展到时间相关的中间变量。使用多状态模型作为数据生成机制的基础，我们通过模拟研究研究了新估计器的性能。此外，我们使用美国国家肺筛查试验数据在肺癌 CT 筛查的背景下说明了所提出的方法。