Using Bayesian methods to test mediators of intervention outcomes in single-case experimental designs

Q2 Social Sciences

Evidence-Based Communication Assessment and Intervention Pub Date : 2020-03-02 DOI:10.1080/17489539.2020.1732029

Milica Miočević, F. Klaassen, Gemma G. M. Geuke, Mariola Moeyaert, M. Maric

{"title":"Using Bayesian methods to test mediators of intervention outcomes in single-case experimental designs","authors":"Milica Miočević, F. Klaassen, Gemma G. M. Geuke, Mariola Moeyaert, M. Maric","doi":"10.1080/17489539.2020.1732029","DOIUrl":null,"url":null,"abstract":"Abstract Single-Case Experimental Designs (SCEDs) have lately been recognized as a valuable alternative to large-group studies. SCEDs form a great tool for the evaluation of treatment effectiveness in heterogeneous and low-incidence conditions, which are common in the field of communication disorders. Mediation analysis is indispensable in treatment research because it informs researchers about the mechanism through which the intervention leads to changes (e.g., communication skills) in the outcome of interest (e.g., developmental outcomes). Despite the increasing popularity of both SCEDs and mediation analysis, there are currently no methods for estimating mediated effects for a single individual. This paper describes how Bayesian piecewise regression analysis can be used for mediation analysis in SCEDs. A Playskin LiftTM dataset from one infant born preterm who is at risk for cognitive developmental delays is used to illustrate two approaches to mediation analysis in SCEDs: Bayesian computation of the mediated effect and Bayesian informative hypothesis testing. Annotated R code is provided so researchers can easily fit the proposed models to their own SCED data set. Advantages and limitations of the method are discussed.","PeriodicalId":39977,"journal":{"name":"Evidence-Based Communication Assessment and Intervention","volume":"52 1","pages":"52 - 68"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evidence-Based Communication Assessment and Intervention","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17489539.2020.1732029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}

引用次数: 9

Abstract

Abstract Single-Case Experimental Designs (SCEDs) have lately been recognized as a valuable alternative to large-group studies. SCEDs form a great tool for the evaluation of treatment effectiveness in heterogeneous and low-incidence conditions, which are common in the field of communication disorders. Mediation analysis is indispensable in treatment research because it informs researchers about the mechanism through which the intervention leads to changes (e.g., communication skills) in the outcome of interest (e.g., developmental outcomes). Despite the increasing popularity of both SCEDs and mediation analysis, there are currently no methods for estimating mediated effects for a single individual. This paper describes how Bayesian piecewise regression analysis can be used for mediation analysis in SCEDs. A Playskin LiftTM dataset from one infant born preterm who is at risk for cognitive developmental delays is used to illustrate two approaches to mediation analysis in SCEDs: Bayesian computation of the mediated effect and Bayesian informative hypothesis testing. Annotated R code is provided so researchers can easily fit the proposed models to their own SCED data set. Advantages and limitations of the method are discussed.

查看原文本刊更多论文

采用贝叶斯方法在单例实验设计中检验干预结果的中介因子

单例实验设计(SCEDs)最近被认为是一种有价值的替代大组研究的方法。SCEDs是评估异质性和低发病率疾病治疗效果的重要工具，这在沟通障碍领域很常见。调解分析在治疗研究中是不可或缺的，因为它使研究人员了解干预导致感兴趣的结果(如发展结果)发生变化(如沟通技巧)的机制。尽管sced和中介分析越来越受欢迎，但目前还没有方法来估计单个个体的中介效应。本文描述了贝叶斯分段回归分析如何用于SCEDs的中介分析。本文利用Playskin LiftTM数据集，对一名存在认知发育迟缓风险的早产婴儿进行了分析，说明了两种中介分析方法:贝叶斯计算中介效应和贝叶斯信息假设检验。提供了带注释的R代码，因此研究人员可以轻松地将提出的模型拟合到他们自己的SCED数据集。讨论了该方法的优点和局限性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Evidence-Based Communication Assessment and Intervention Social Sciences-Linguistics and Language

CiteScore

1.60

自引率

0.00%

发文量

期刊介绍： Evidence-Based Communication Assessment and Intervention (EBCAI) brings together professionals who work in clinical and educational practice as well as researchers from all disciplines to promote evidence-based practice (EBP) in serving individuals with communication impairments. The primary aims of EBCAI are to: Promote evidence-based practice (EBP) in communication assessment and intervention; Appraise the latest and best communication assessment and intervention studies so as to facilitate the use of research findings in clinical and educational practice; Provide a forum for discussions that advance EBP; and Disseminate research on EBP. We target speech-language pathologists, special educators, regular educators, applied behavior analysts, clinical psychologists, physical therapists, and occupational therapists who serve children or adults with communication impairments.