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评估公共卫生干预措施:3. 减少混杂偏倚的两阶段设计——两全其美。

Evaluating Public Health Interventions: 3. The Two-Stage Design for Confounding Bias Reduction-Having Your Cake and Eating It Two.

作者信息

Spiegelman Donna, Rivera-Rodriguez Claudia L, Haneuse Sebastien

机构信息

Donna Spiegelman and Claudia L. Rivera-Rodriguez are with the Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA. Sebastien Haneuse is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health.

出版信息

Am J Public Health. 2016 Jul;106(7):1223-6. doi: 10.2105/AJPH.2016.303250.

Abstract

In public health evaluations, confounding bias in the estimate of the intervention effect will typically threaten the validity of the findings. It is a common misperception that the only way to avoid this bias is to measure detailed, high-quality data on potential confounders for every intervention participant, but this strategy for adjusting for confounding bias is often infeasible. Rather than ignoring confounding altogether, the two-phase design and analysis-in which detailed high-quality confounding data are obtained among a small subsample-can be considered. We describe the two-stage design and analysis approach, and illustrate its use in the evaluation of an intervention conducted in Dar es Salaam, Tanzania, of an enhanced community health worker program to improve antenatal care uptake.

摘要

在公共卫生评估中,干预效果估计中的混杂偏倚通常会威胁研究结果的有效性。一种常见的误解是,避免这种偏倚的唯一方法是为每个干预参与者测量关于潜在混杂因素的详细、高质量数据,但这种调整混杂偏倚的策略往往不可行。与其完全忽略混杂因素,可考虑采用两阶段设计和分析方法,即在一个小的子样本中获取详细的高质量混杂数据。我们描述了两阶段设计和分析方法,并举例说明其在坦桑尼亚达累斯萨拉姆进行的一项干预评估中的应用,该干预是一个强化社区卫生工作者项目,旨在提高产前护理的利用率。

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