Economic analysis of a multi-site prevention program: assessment of program costs and characterizing site-level variability.
Economic analysis of a multi-site prevention program: assessment of program costs and characterizing site-level variability.
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多地点预防计划的经济分析：评估计划成本并描述地点级别的变异性。
DOI：
10.1007/s11121-012-0316-z
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Brody,GeneH
中科院分区：
文献类型：
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作者：
Corso,PhaedraS;Ingels,JustinB;Kogan,StevenM;Foster,EMichael;Chen,Yi-Fu;Brody,GeneH
预防性干预措施的方案成本分析通常在方法上有一些困难。为了确定平均总成本并正确地描述变异性，人们通常必须处理小样本量，偏斜分布，特别是缺失数据。处理缺失数据的标准方法（如多重插补）可能会受到样本量小、缺乏适当协变量或用于处理缺失数据的方法细节太少的影响。在这项研究中，我们估计总计划成本的预防试验评估强大的非洲裔美国家庭，青少年计划。这种干预措施的重点是预防药物滥用和危险性行为。为了解释在评估项目成本时的缺失数据，我们将多重插补与概率敏感性分析进行了比较。后一种方法使用收集的成本数据来创建围绕每个输入参数的分布。我们发现，采用多重插补方法，平均（95%置信区间）增量差异为2，149美元（397美元，3，901美元）。采用概率敏感性分析方法，增量差异为2 583美元（778美元，4 346美元）。虽然该计划的真实成本是未知的，概率敏感性分析可能是一个更可行的替代方案，用于捕捉在处理缺失数据时，特别是在小样本量和缺乏强有力的预测变量的方案成本估计的变化。此外，概率敏感性分析方法产生的更大标准误差可能表明其有能力捕获更多数据变异性，从而更好地向政策制定者提供有关干预潜在真实成本的信息。
Programmatic cost analyses of preventive interventions commonly have a number of methodological difficulties. To determine the mean total costs and properly characterize variability, one often has to deal with small sample sizes, skewed distributions, and especially missing data. Standard approaches for dealing with missing data such as multiple imputation may suffer from a small sample size, a lack of appropriate covariates, or too few details around the method used to handle the missing data. In this study, we estimate total programmatic costs for a prevention trial evaluating the Strong African American Families-Teen program. This intervention focuses on the prevention of substance abuse and risky sexual behavior. To account for missing data in the assessment of programmatic costs we compare multiple imputation to probabilistic sensitivity analysis. The latter approach uses collected cost data to create a distribution around each input parameter. We found that with the multiple imputation approach, the mean (95 % confidence interval) incremental difference was $2,149 ($397, $3,901). With the probabilistic sensitivity analysis approach, the incremental difference was $2,583 ($778, $4,346). Although thetruecost of the program is unknown, probabilistic sensitivity analysis may be a more viable alternative for capturing variability in estimates of programmatic costs when dealing with missing data, particularly with small sample sizes and the lack of strong predictor variables. Further, the larger standard errors produced by the probabilistic sensitivity analysis method may signal its ability to capture more of the variability in the data, thus better informing policymakers on the potentiallytruecost of the intervention.