{"title":"Bioequivalence data analysis.","authors":"Gowooni Park, Hyungsub Kim, Kyun-Seop Bae","doi":"10.12793/tcp.2020.28.e20","DOIUrl":null,"url":null,"abstract":"<p><p>SAS<sup>®</sup> is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS<sup>®</sup> for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS<sup>®</sup>. The main SAS<sup>®</sup> procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are \"sasLM\" and \"nlme\" respectively. For fixed effects only or balanced data, the SAS<sup>®</sup> PROC GLM and R \"sasLM\" provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS<sup>®</sup> PROC MIXED and R \"nlme\" are better for providing estimates without bias. The SAS<sup>®</sup> and R scripts are provided for convenience.</p>","PeriodicalId":23288,"journal":{"name":"Translational and Clinical Pharmacology","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/50/14/tcp-28-175.PMC7781810.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational and Clinical Pharmacology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12793/tcp.2020.28.e20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/12/17 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
SAS® is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS® for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS®. The main SAS® procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are "sasLM" and "nlme" respectively. For fixed effects only or balanced data, the SAS® PROC GLM and R "sasLM" provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS® PROC MIXED and R "nlme" are better for providing estimates without bias. The SAS® and R scripts are provided for convenience.
期刊介绍：
Translational and Clinical Pharmacology (Transl Clin Pharmacol, TCP) is the official journal of the Korean Society for Clinical Pharmacology and Therapeutics (KSCPT). TCP is an interdisciplinary journal devoted to the dissemination of knowledge relating to all aspects of translational and clinical pharmacology. The categories for publication include pharmacokinetics (PK) and drug disposition, drug metabolism, pharmacodynamics (PD), clinical trials and design issues, pharmacogenomics and pharmacogenetics, pharmacometrics, pharmacoepidemiology, pharmacovigilence, and human pharmacology. Studies involving animal models, pharmacological characterization, and clinical trials are appropriate for consideration.
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