Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness.
Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness.
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通过缺失资格数据的目标试验仿真和偏见：对帕利维珠单抗研究的应用，以预防婴儿呼吸道疾病引起的住院治疗。
DOI：
10.1093/aje/kwac202
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发表时间：
2023-04-06
影响因子：
5
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中科院分区：
文献类型：
--
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目标试验模拟（TTE）将随机对照试验的原则应用于观察数据集的因果分析。TTE中很少考虑的一个挑战是，如果试验合格性定义中涉及的变量缺失，可能会出现偏倚的来源。我们强调的偏见模式时，可能会出现估计点暴露的因果关系时，限制目标试验的个人完整的资格数据。模拟考虑现实的情况下，影响资格的变量修改的因果关系的影响，暴露和随机或非随机失踪。我们讨论了解决这些偏倚模式的方法，即：1）控制由合格性缺失数据引起的碰撞机偏倚，以及2）在选择目标试验之前将合格性变量的缺失值插补。将结果与忽略缺失合格性影响进行TTE时的结果进行比较。帕利珠单抗是一种单克隆抗体，推荐用于预防高危婴儿因呼吸道合胞病毒而住院的研究，用于说明。
Target trial emulation (TTE) applies the principles of randomized controlled trials to the causal analysis of observational data sets. One challenge that is rarely considered in TTE is the sources of bias that may arise if the variables involved in the definition of eligibility for the trial are missing. We highlight patterns of bias that might arise when estimating the causal effect of a point exposure when restricting the target trial to individuals with complete eligibility data. Simulations consider realistic scenarios where the variables affecting eligibility modify the causal effect of the exposure and are missing at random or missing not at random. We discuss means to address these patterns of bias, namely: 1) controlling for the collider bias induced by the missing data on eligibility, and 2) imputing the missing values of the eligibility variables prior to selection into the target trial. Results are compared with the results when TTE is performed ignoring the impact of missing eligibility. A study of palivizumab, a monoclonal antibody recommended for the prevention of respiratory hospital admissions due to respiratory syncytial virus in high-risk infants, is used for illustration.
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