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首页> 外文期刊>Medical decision making: An international journal of the Society for Medical Decision Making >Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: An application to cluster randomized trials
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Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: An application to cluster randomized trials

机译：使用分层研究的数据在成本效益分析中处理缺失数据的多种插补方法：对随机试验进行聚类的应用

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Purpose. Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA) in CEAs that use CRTs. Methods. We consider a multilevel MI approach compatible with multilevel analytical models for CEAs that use CRTs. We took fully observed data from a CEA that evaluated an intervention to improve diagnosis of active labor in primiparous women using a CRT (2078 patients, 14 clusters). We generated scenarios with missing costs and outcomes that differed, for example, according to the proportion with missing data (10%-50%), the covariates that predicted missing data (individual, cluster-level), and the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). We estimated incremental net benefits (INBs) for each approach and compared them with the estimates from the fully observed data, the "true" INBs. Results. When costs and outcomes were assumed to be MCAR, the INBs for each approach were similar to the true estimates. When data were MAR, the point estimates from the CCA differed from the true estimates. Multilevel MI provided point estimates and standard errors closer to the true values than did single-level MI across all settings, including those in which a high proportion of observations had cost and outcome data MAR and when data were MNAR. Conclusions. Multilevel MI accommodates the multilevel structure of the data in CEAs that use cluster trials and provides accurate cost-effectiveness estimates across the range of circumstances considered.
机译：目的。在成本效益分析（CEA）中，已提出多重插补（MI）来处理丢失的数据。在使用聚类随机试验（CRT）的CEA中，归因模型应与分析模型一样，应识别数据的层次结构。本文对比了使用CRT的CEA中的单级MI和完整案例分析（CCA）来识别集群的多级MI方法。方法。我们考虑与使用CRT的CEA的多层次分析模型兼容的多层次MI方法。我们从CEA那里获得了充分观察到的数据，该数据评估了使用CRT改善初产妇女主动劳动诊断的干预措施（2078例患者，14个组）。我们生成了成本和结果缺失的方案，例如，根据缺失数据的比例（10％-50％），预测缺失数据的协变量（个体，集群级别）以及缺失机制：完全缺失随机（MCAR），随机（MAR）丢失或非随机（MNAR）丢失。我们估算了每种方法的增量净收益（INB），并将它们与充分观察到的数据（“真实” INB）的估算值进行了比较。结果。当成本和结果被假定为MCAR时，每种方法的INB均与真实估计值相似。当数据为MAR时，CCA的点估计值与真实估计值不同。在所有设置中，多级MI提供的点估计值和标准误差都比单级MI的更接近真实值，包括那些具有较高成本和结果数据MAR且观测值为MNAR的观测值。结论多层MI适应使用聚类试验的CEA中数据的多层结构，并在考虑的各种情况下提供准确的成本效益估算。

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