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Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects

BACKGROUND: In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To...

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Autores principales: Dai, Tianjiao, Shete, Sanjay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006275/
https://www.ncbi.nlm.nih.gov/pubmed/27578254
http://dx.doi.org/10.1186/s12874-016-0202-7
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author Dai, Tianjiao
Shete, Sanjay
author_facet Dai, Tianjiao
Shete, Sanjay
author_sort Dai, Tianjiao
collection PubMed
description BACKGROUND: In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To reduce the side effects and costs associated with first-stage interventions in a SMART design, we proposed a novel time-varying SMART design in which individuals are re-randomized to the second-stage interventions as soon as a pre-fixed intermediate response is observed. With this strategy, the duration of the first-stage intervention will vary. METHODS: We developed a time-varying mixed effects model and a joint model that allows for modeling the outcomes of interest (intermediate and final) and the random durations of the first-stage interventions simultaneously. The joint model borrows strength from the survival sub-model in which the duration of the first-stage intervention (i.e., time to response to the first-stage intervention) is modeled. We performed a simulation study to evaluate the statistical properties of these models. RESULTS: Our simulation results showed that the two modeling approaches were both able to provide good estimations of the means of the final outcomes of all the embedded interventions in a SMART. However, the joint modeling approach was more accurate for estimating the coefficients of first-stage interventions and time of the intervention. CONCLUSION: We conclude that the joint modeling approach provides more accurate parameter estimates and a higher estimated coverage probability than the single time-varying mixed effects model, and we recommend the joint model for analyzing data generated from time-varying SMART designs. In addition, we showed that the proposed time-varying SMART design is cost-efficient and equally effective in selecting the optimal embedded adaptive intervention as the standard SMART design. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0202-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-50062752016-09-01 Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects Dai, Tianjiao Shete, Sanjay BMC Med Res Methodol Research Article BACKGROUND: In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To reduce the side effects and costs associated with first-stage interventions in a SMART design, we proposed a novel time-varying SMART design in which individuals are re-randomized to the second-stage interventions as soon as a pre-fixed intermediate response is observed. With this strategy, the duration of the first-stage intervention will vary. METHODS: We developed a time-varying mixed effects model and a joint model that allows for modeling the outcomes of interest (intermediate and final) and the random durations of the first-stage interventions simultaneously. The joint model borrows strength from the survival sub-model in which the duration of the first-stage intervention (i.e., time to response to the first-stage intervention) is modeled. We performed a simulation study to evaluate the statistical properties of these models. RESULTS: Our simulation results showed that the two modeling approaches were both able to provide good estimations of the means of the final outcomes of all the embedded interventions in a SMART. However, the joint modeling approach was more accurate for estimating the coefficients of first-stage interventions and time of the intervention. CONCLUSION: We conclude that the joint modeling approach provides more accurate parameter estimates and a higher estimated coverage probability than the single time-varying mixed effects model, and we recommend the joint model for analyzing data generated from time-varying SMART designs. In addition, we showed that the proposed time-varying SMART design is cost-efficient and equally effective in selecting the optimal embedded adaptive intervention as the standard SMART design. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0202-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-30 /pmc/articles/PMC5006275/ /pubmed/27578254 http://dx.doi.org/10.1186/s12874-016-0202-7 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Dai, Tianjiao
Shete, Sanjay
Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title_full Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title_fullStr Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title_full_unstemmed Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title_short Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
title_sort time-varying smart design and data analysis methods for evaluating adaptive intervention effects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5006275/
https://www.ncbi.nlm.nih.gov/pubmed/27578254
http://dx.doi.org/10.1186/s12874-016-0202-7
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