Department of Psychology, 5620McGill University, Montreal, QC, Canada.
Department of Psychology, 9373Humboldt-Universitätzu Berlin, Berlin, Germany.
Eval Health Prof. 2022 Mar;45(1):36-53. doi: 10.1177/01632787211071136. Epub 2022 Feb 26.
Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.
单病例实验设计（SCEDs）越来越被认为是群组设计的一种有价值的替代方法。在 SCEDs 中，中介分析很有用，因为它可以告诉研究人员干预如何通过潜在机制影响结果。然而，最近才提出了在 SCEDs 中进行中介分析的方法。此外，目标行为的重复测量会带来自相关和缺失数据的挑战。本文旨在通过（1）评估三种用于对 SCEDs 中分段回归分析中的自相关进行建模的方法，即 Newey-West（NW）估计、可行广义最小二乘法（FGLS）估计和在误差项中显式建模一阶自回归结构（AR(1)），以及（2）在完全随机缺失数据的情况下评估多重插补，来扩展在 SCEDs 中估计间接效应的方法。FGLS 和 AR(1)在效率、I 型错误率和覆盖率方面优于 NW 和 OLS 估计，而 OLS 在更大样本量方面优于其他方法。所有方法的性能在 0%和 20%缺失数据条件下都是一致的。50%的缺失数据导致了不理想的功效和有偏差的估计。基于这些发现，我们为应用研究人员提供了建议。
