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SAS^® Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models. SAS®宏，用于计算两波和三波纵向模型中的因果中介效应。

SAS global forum Pub Date : 2018-01-01

Matthew J Valente, David P MacKinnon

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引用次数: 0

SAS® Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design. SAS®宏用于计算前测后测对照组设计中的中介效应。

SAS global forum Pub Date : 2017-01-01

Matthew J Valente, David P MacKinnon

{"title":"SAS® Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design.","authors":"Matthew J Valente, David P MacKinnon","doi":"","DOIUrl":"","url":null,"abstract":"Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effect estimates. Cause-and-effect inferences are particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator (MacKinnon, 2008). The focus of this paper is how incorporating longitudinal measures of the mediating and outcome variables aides in the causal interpretation of mediated effects. This paper provides useful SAS® tools for designing adequately powered studies to detect the mediated effect. Three SAS macros were developed using the powerful but easy-to-use REG, CALIS, and SURVEYSELECT procedures to do the following: (1) implement popular statistical models for estimating the mediated effect in the pretest-posttest control group design; (2) conduct a prospective power analysis for determining the required sample size for detecting the mediated effect; and (3) conduct a retrospective power analysis for studies that have already been conducted and a required sample to detect an observed effect is desired. We demonstrate the use of these three macros with an example.","PeriodicalId":90722,"journal":{"name":"SAS global forum","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133302/pdf/nihms939374.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36488891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DATA CLEANING: LONGITUDINAL STUDY CROSS-VISIT CHECKS. 数据清理:纵向研究交叉访问检查。

SAS global forum Pub Date : 2014-03-01

Lauren Parlett

引用次数: 0