评估因果影响方法在丙型肝炎病毒治疗作为预防措施的观察性研究中的效力。
Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention.
作者信息
Samartsidis Pantelis, Martin Natasha N, De Gruttola Victor, De Vocht Frank, Hutchinson Sharon, Lok Judith J, Puenpatom Amy, Wang Rui, Hickman Matthew, De Angelis Daniela
机构信息
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
University of California San Diego, San Diego, USA.
出版信息
Stat Commun Infect Dis. 2021 Oct 11;13(1):20200005. doi: 10.1515/scid-2020-0005. eCollection 2021 Jan 1.
OBJECTIVES
The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems.
METHODS
Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem.
RESULTS
We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated.
CONCLUSIONS
The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.
目的
因果影响方法（CIM）最近被引入，用于使用观察性时间序列数据评估二元干预措施。CIM在实际应用中很有吸引力，因为它可以调整时间趋势并考虑未观察到的混杂因素的可能性。然而，该方法最初是为涉及大型数据集的应用而开发的，因此其在小型流行病学研究中的潜力仍不明确。此外，测量误差对CIM性能的影响尚未得到研究。这项工作的目的是研究这两个未解决的问题。
方法
基于英国现有的丙型肝炎病毒（HCV）监测数据集，我们进行模拟实验，以研究数据的几个特征对CIM性能的影响。此外，我们量化测量误差对CIM性能的影响，并扩展该方法以处理此问题。
结果
我们确定了影响CIM检测干预效果能力的多个数据特征，包括时间序列的长度、结果的变异性以及治疗组单元结果与对照组结果之间的相关程度。我们表明，测量误差会在估计的干预效果中引入偏差，并严重降低CIM的效能。使用扩展的CIM，可以减轻其中一些不利影响。
结论
CIM在公共卫生干预中可以提供令人满意的效能。在存在测量误差的情况下，该方法可能会产生误导性结果。
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