NCSU Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695-8203, USA.
Prev Sci. 2010 Sep;11(3):239-51. doi: 10.1007/s11121-010-0169-2.
This paper focuses on the impact of selection bias in the context of extended, community-based prevention trials that attempt to "unpack" intervention effects and analyze mechanisms of change. Relying on dose-response analyses as the most general form of such efforts, this study provides two examples of how selection bias can affect the estimation of treatment effects. In Example 1, we describe an actual intervention in which selection bias was believed to influence the dose-response relation of an adaptive component in a preventive intervention for young children with severe behavior problems. In Example 2, we conduct a series of Monte Carlo simulations to illustrate just how severely selection bias can affect estimates in a dose-response analysis when the factors that affect dose are not recorded. We also assess the extent to which selection bias is ameliorated by the use of pretreatment covariates. We examine the implications of these examples and review trial design, data collection, and data analysis factors that can reduce selection bias in efforts to understand how preventive interventions have the effects they do.
本文聚焦于扩展的、基于社区的预防试验中选择偏差的影响，这些试验试图“剖析”干预效果并分析变化的机制。本研究依赖剂量-反应分析作为此类努力的最一般形式，提供了两个示例，说明选择偏差如何影响治疗效果的估计。在示例 1 中，我们描述了一个实际的干预，其中选择偏差被认为会影响预防干预中一个适应性组件的剂量-反应关系，该干预针对有严重行为问题的幼儿。在示例 2 中，我们进行了一系列蒙特卡罗模拟，以说明当影响剂量的因素未被记录时，选择偏差会如何严重影响剂量-反应分析中的估计。我们还评估了使用预处理协变量来减轻选择偏差的程度。我们探讨了这些示例的意义，并回顾了试验设计、数据收集和数据分析因素，这些因素可以减少理解预防干预产生效果的努力中的选择偏差。
