Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model
Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT × COV. In...
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description | Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT
×
COV. In detecting TRT
×
COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT
×
COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood,
2021
). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT
×
COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship. |
doi_str_mv | 10.3758/s13428-023-02138-w |
format | Article |
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×
COV. In detecting TRT
×
COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT
×
COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood,
2021
). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT
×
COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.</description><identifier>ISSN: 1554-3528</identifier><identifier>EISSN: 1554-3528</identifier><identifier>DOI: 10.3758/s13428-023-02138-w</identifier><identifier>PMID: 37558925</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Behavioral Science and Psychology ; Cluster Analysis ; Cognitive Psychology ; Computer Simulation ; Humans ; Psychology ; Randomized Controlled Trials as Topic ; Statistical models</subject><ispartof>Behavior research methods, 2024-03, Vol.56 (3), p.2094-2113</ispartof><rights>The Psychonomic Society, Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Psychonomic Society, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-847624e45d19c81b87b6d811647d9dc475242feb073daf6a1aa3c8da4f9de9243</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3758/s13428-023-02138-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3758/s13428-023-02138-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37558925$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cho, Sun-Joo</creatorcontrib><creatorcontrib>Preacher, Kristopher J.</creatorcontrib><creatorcontrib>Yaremych, Haley E.</creatorcontrib><creatorcontrib>Naveiras, Matthew</creatorcontrib><creatorcontrib>Fuchs, Douglas</creatorcontrib><creatorcontrib>Fuchs, Lynn S.</creatorcontrib><title>Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model</title><title>Behavior research methods</title><addtitle>Behav Res</addtitle><addtitle>Behav Res Methods</addtitle><description>Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT
×
COV. In detecting TRT
×
COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT
×
COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood,
2021
). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT
×
COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.</description><subject>Behavioral Science and Psychology</subject><subject>Cluster Analysis</subject><subject>Cognitive Psychology</subject><subject>Computer Simulation</subject><subject>Humans</subject><subject>Psychology</subject><subject>Randomized Controlled Trials as Topic</subject><subject>Statistical models</subject><issn>1554-3528</issn><issn>1554-3528</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1uFiEUhonR2Fq9AReGxI2b0eFvhlmaRluTGje6JgwcPmkYqMC0_bwVb1a-Tv2JCxeEQ3jOc07yIvSc9K_ZKOSbQhinsuspa4cw2d08QMdECN4xQeXDv-oj9KSUy75nkhL-GB21diEnKo7Rj4_JQvBxh6919nr2wdc99hHXDLouECsG58DUgl3K2IS1VMg462jT4r-DxSbFmlMIrazNEApey8E377tNGQCXJaX6Fbs1mupTLIcBGu8gQtbhzqKt9dVfA178bXsuh62eokeu-eDZ_X2Cvrx_9_n0vLv4dPbh9O1FZxgdaif5OFAOXFgyGUlmOc6DlYQMfLSTNXwUlFMHcz8yq92gidbMSKu5myxMlLMT9GrzXuX0bYVS1eKLgRB0hLQWRSWXknPRy4a-_Ae9TGuObTvFekZGxggdG0U3yuRUSganrrJfdN4r0qtDdGqLTrXo1F106qY1vbhXr_MC9nfLr6wawDagtK-4g_xn9n-0PwG2HaiW</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Cho, Sun-Joo</creator><creator>Preacher, Kristopher J.</creator><creator>Yaremych, Haley E.</creator><creator>Naveiras, Matthew</creator><creator>Fuchs, Douglas</creator><creator>Fuchs, Lynn S.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>4T-</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>20240301</creationdate><title>Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model</title><author>Cho, Sun-Joo ; Preacher, Kristopher J. ; Yaremych, Haley E. ; Naveiras, Matthew ; Fuchs, Douglas ; Fuchs, Lynn S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-847624e45d19c81b87b6d811647d9dc475242feb073daf6a1aa3c8da4f9de9243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Behavioral Science and Psychology</topic><topic>Cluster Analysis</topic><topic>Cognitive Psychology</topic><topic>Computer Simulation</topic><topic>Humans</topic><topic>Psychology</topic><topic>Randomized Controlled Trials as Topic</topic><topic>Statistical models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cho, Sun-Joo</creatorcontrib><creatorcontrib>Preacher, Kristopher J.</creatorcontrib><creatorcontrib>Yaremych, Haley E.</creatorcontrib><creatorcontrib>Naveiras, Matthew</creatorcontrib><creatorcontrib>Fuchs, Douglas</creatorcontrib><creatorcontrib>Fuchs, Lynn S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Docstoc</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Behavior research methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cho, Sun-Joo</au><au>Preacher, Kristopher J.</au><au>Yaremych, Haley E.</au><au>Naveiras, Matthew</au><au>Fuchs, Douglas</au><au>Fuchs, Lynn S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model</atitle><jtitle>Behavior research methods</jtitle><stitle>Behav Res</stitle><addtitle>Behav Res Methods</addtitle><date>2024-03-01</date><risdate>2024</risdate><volume>56</volume><issue>3</issue><spage>2094</spage><epage>2113</epage><pages>2094-2113</pages><issn>1554-3528</issn><eissn>1554-3528</eissn><abstract>Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT
×
COV. In detecting TRT
×
COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT
×
COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood,
2021
). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT
×
COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37558925</pmid><doi>10.3758/s13428-023-02138-w</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Behavioral Science and Psychology Cluster Analysis Cognitive Psychology Computer Simulation Humans Psychology Randomized Controlled Trials as Topic Statistical models |
title | Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model |
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