Stepped Wedge Designs: Insights from a Design of Experiments Perspective. Matthews, J. N. S. & Forbes, A. B. Stat Med. doi abstract bibtex Stepped wedge designs (SWDs) have received considerable attention recently, as they are potentially a useful way to assess new treatments in areas such as health services implementation. Because allocation is usually by cluster, SWDs are often viewed as a form of cluster-randomized trial. However, since the treatment within a cluster changes during the course of the study, they can also be viewed as a form of crossover design. This article explores SWDs from the perspective of crossover trials and designed experiments more generally. We show that the treatment effect estimator in a linear mixed effects model can be decomposed into a weighted mean of the estimators obtained from (1) regarding an SWD as a conventional row-column design and (2) a so-called vertical analysis, which is a row-column design with row effects omitted. This provides a precise representation of '' horizontal'' and '' vertical'' comparisons, respectively, which to date have appeared without formal description in the literature. This decomposition displays a sometimes surprising way the analysis corrects for the partial confounding between time and treatment effects. The approach also permits the quantification of the loss of efficiency caused by mis-specifying the correlation parameter in the mixed-effects model. Optimal extensions of the vertical analysis are obtained, and these are shown to be highly inefficient for values of the within-cluster dependence that are likely to be encountered in practice. Some recently described extensions to the classic SWD incorporating multiple treatments are also compared using the experimental design framework.
@article{mat17ste,
title = {Stepped Wedge Designs: Insights from a Design of Experiments Perspective},
abstract = {Stepped wedge designs (SWDs) have received considerable attention recently, as they are potentially a useful way to assess new treatments in areas such as health services implementation. Because allocation is usually by cluster, SWDs are often viewed as a form of cluster-randomized trial. However, since the treatment within a cluster changes during the course of the study, they can also be viewed as a form of crossover design. This article explores SWDs from the perspective of crossover trials and designed experiments more generally. We show that the treatment effect estimator in a linear mixed effects model can be decomposed into a weighted mean of the estimators obtained from (1) regarding an SWD as a conventional row-column design and (2) a so-called vertical analysis, which is a row-column design with row effects omitted. This provides a precise representation of '' horizontal'' and '' vertical'' comparisons, respectively, which to date have appeared without formal description in the literature. This decomposition displays a sometimes surprising way the analysis corrects for the partial confounding between time and treatment effects. The approach also permits the quantification of the loss of efficiency caused by mis-specifying the correlation parameter in the mixed-effects model. Optimal extensions of the vertical analysis are obtained, and these are shown to be highly inefficient for values of the within-cluster dependence that are likely to be encountered in practice. Some recently described extensions to the classic SWD incorporating multiple treatments are also compared using the experimental design framework.},
journal = {Stat Med},
doi = {10.1002/sim.7403},
author = {Matthews, J. N. S. and Forbes, A. B.},
keywords = {cluster-randomized-trial,stepped-wedge,crossover},
citeulike-article-id = {14410359},
citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.7403},
posted-at = {2017-08-08 12:50:42},
priority = {2}
}
Downloads: 0
{"_id":"Ft9eb6zhMPBGc3PWw","bibbaseid":"matthews-forbes-steppedwedgedesignsinsightsfromadesignofexperimentsperspective","downloads":0,"creationDate":"2018-06-23T20:06:33.376Z","title":"Stepped Wedge Designs: Insights from a Design of Experiments Perspective","author_short":["Matthews, J. N. S.","Forbes, A. B."],"year":null,"bibtype":"article","biburl":"http://hbiostat.org/bib/harrelfe.bib","bibdata":{"bibtype":"article","type":"article","title":"Stepped Wedge Designs: Insights from a Design of Experiments Perspective","abstract":"Stepped wedge designs (SWDs) have received considerable attention recently, as they are potentially a useful way to assess new treatments in areas such as health services implementation. Because allocation is usually by cluster, SWDs are often viewed as a form of cluster-randomized trial. However, since the treatment within a cluster changes during the course of the study, they can also be viewed as a form of crossover design. This article explores SWDs from the perspective of crossover trials and designed experiments more generally. We show that the treatment effect estimator in a linear mixed effects model can be decomposed into a weighted mean of the estimators obtained from (1) regarding an SWD as a conventional row-column design and (2) a so-called vertical analysis, which is a row-column design with row effects omitted. This provides a precise representation of '' horizontal'' and '' vertical'' comparisons, respectively, which to date have appeared without formal description in the literature. This decomposition displays a sometimes surprising way the analysis corrects for the partial confounding between time and treatment effects. The approach also permits the quantification of the loss of efficiency caused by mis-specifying the correlation parameter in the mixed-effects model. Optimal extensions of the vertical analysis are obtained, and these are shown to be highly inefficient for values of the within-cluster dependence that are likely to be encountered in practice. Some recently described extensions to the classic SWD incorporating multiple treatments are also compared using the experimental design framework.","journal":"Stat Med","doi":"10.1002/sim.7403","author":[{"propositions":[],"lastnames":["Matthews"],"firstnames":["J.","N.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Forbes"],"firstnames":["A.","B."],"suffixes":[]}],"keywords":"cluster-randomized-trial,stepped-wedge,crossover","citeulike-article-id":"14410359","citeulike-linkout-0":"http://dx.doi.org/10.1002/sim.7403","posted-at":"2017-08-08 12:50:42","priority":"2","bibtex":"@article{mat17ste,\n title = {Stepped Wedge Designs: Insights from a Design of Experiments Perspective},\n abstract = {Stepped wedge designs (SWDs) have received considerable attention recently, as they are potentially a useful way to assess new treatments in areas such as health services implementation. Because allocation is usually by cluster, SWDs are often viewed as a form of cluster-randomized trial. However, since the treatment within a cluster changes during the course of the study, they can also be viewed as a form of crossover design. This article explores SWDs from the perspective of crossover trials and designed experiments more generally. We show that the treatment effect estimator in a linear mixed effects model can be decomposed into a weighted mean of the estimators obtained from (1) regarding an SWD as a conventional row-column design and (2) a so-called vertical analysis, which is a row-column design with row effects omitted. This provides a precise representation of '' horizontal'' and '' vertical'' comparisons, respectively, which to date have appeared without formal description in the literature. This decomposition displays a sometimes surprising way the analysis corrects for the partial confounding between time and treatment effects. The approach also permits the quantification of the loss of efficiency caused by mis-specifying the correlation parameter in the mixed-effects model. Optimal extensions of the vertical analysis are obtained, and these are shown to be highly inefficient for values of the within-cluster dependence that are likely to be encountered in practice. Some recently described extensions to the classic SWD incorporating multiple treatments are also compared using the experimental design framework.},\n journal = {Stat Med},\n doi = {10.1002/sim.7403},\n author = {Matthews, J. N. S. and Forbes, A. B.},\n keywords = {cluster-randomized-trial,stepped-wedge,crossover},\n citeulike-article-id = {14410359},\n citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.7403},\n posted-at = {2017-08-08 12:50:42},\n priority = {2}\n}\n\n","author_short":["Matthews, J. N. S.","Forbes, A. B."],"key":"mat17ste","id":"mat17ste","bibbaseid":"matthews-forbes-steppedwedgedesignsinsightsfromadesignofexperimentsperspective","role":"author","urls":{},"keyword":["cluster-randomized-trial","stepped-wedge","crossover"],"downloads":0},"search_terms":["stepped","wedge","designs","insights","design","experiments","perspective","matthews","forbes"],"keywords":["cluster-randomized-trial","crossover","stepped-wedge"],"authorIDs":[],"dataSources":["mEQakjn8ggpMsnGJi"]}