Department of Educational and Psychological Studies, University of South Florida.
Faculty of Psychology and Educational Sciences.
Psychol Methods. 2014 Dec;19(4):493-510. doi: 10.1037/a0037038. Epub 2014 Jun 16.
Traditionally, average causal effects from multiple-baseline data are estimated by aggregating individual causal effect estimates obtained through within-series comparisons of treatment phase trajectories to baseline extrapolations. Concern that these estimates may be biased due to event effects, such as history and maturation, motivates our proposal of a between-series estimator that contrasts participants in the treatment to those in the baseline phase. Accuracy of the new method was assessed and compared in a series of simulation studies where participants were randomly assigned to intervention start points. The within-series estimator was found to have greater power to detect treatment effects but also to be biased due to event effects, leading to faulty causal inferences. The between-series estimator remained unbiased and controlled the Type I error rate independent of event effects. Because the between-series estimator is unbiased under different assumptions, the 2 estimates complement each other, and the difference between them can be used to detect inaccuracies in the modeling assumptions. The power to detect inaccuracies associated with event effects was found to depend on the size and type of event effect. We empirically illustrate the methods using a real data set and then discuss implications for researchers planning multiple-baseline studies.
传统上，通过对治疗阶段轨迹与基线外推的系列内比较来获得个体因果效应估计，从而对多基线数据中的平均因果效应进行估计。由于事件效应（如历史和成熟）的影响，这些估计可能存在偏差，这引起了我们提出对比治疗阶段和基线阶段参与者的系列间估计器的动机。在一系列参与者被随机分配到干预起始点的模拟研究中，评估并比较了新方法的准确性。发现系列内估计器具有更大的检测治疗效果的能力，但也由于事件效应而存在偏差，导致错误的因果推断。系列间估计器保持无偏且独立于事件效应控制了Ⅰ型错误率。由于系列间估计器在不同假设下是无偏的，因此这两个估计相互补充，并且它们之间的差异可用于检测建模假设的不准确之处。与事件效应相关的检测不准确的能力发现取决于事件效应的大小和类型。我们使用真实数据集实证说明了这些方法，然后讨论了对计划多基线研究的研究人员的影响。
