Baek Eunkyeng, Ferron John M
Texas A&M University , College Station, TX, USA.
University of South Florida , Tampa, FL, USA.
Dev Neurorehabil. 2021 Feb;24(2):130-143. doi: 10.1080/17518423.2020.1858455. Epub 2021 Jan 3.
There is a growing interest in the potential benefits of applying Bayesian estimation for multilevel models of SCED data. Methodological studies have shown that Bayesian estimation resolves convergence issues, can be adequate for the small sample, and can improve the accuracy of the variance components. Despite the potential benefits, the lack of accessibility to software codes makes it difficult for applied researchers to implement Bayesian estimation in their studies. The purpose of this article is to illustrate a feasible way to implement Bayesian estimation using OpenBUGS software to analyze a complex SCED model where within-participants variability and autocorrelation may differ across cases. By using extracted data from a published study, step-by-step guidance in analyzing the data using OpenBUGS software is provided, including (1) model specification, (2) prior distributions, (3) data entering, (4) model estimation, (5) convergence criteria, and (6) posterior inferences and interpretations. Full codes for the analysis are provided.
人们越来越关注将贝叶斯估计应用于单次病例系列数据（SCED）的多级模型的潜在益处。方法学研究表明，贝叶斯估计解决了收敛问题，适用于小样本，并能提高方差成分的准确性。尽管有潜在益处，但软件代码难以获取，使得应用研究人员在其研究中难以实施贝叶斯估计。本文的目的是说明一种使用OpenBUGS软件实施贝叶斯估计的可行方法，以分析一个复杂的SCED模型，其中病例间参与者内部的变异性和自相关性可能不同。通过使用从一项已发表研究中提取的数据，提供了使用OpenBUGS软件分析数据的逐步指导，包括（1）模型规范，（2）先验分布，（3）数据输入，（4）模型估计，（5）收敛标准，以及（6）后验推断和解释。还提供了完整的分析代码。
