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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2016
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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. |
format | Online Article Text |
id | pubmed-5006275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT daitianjiao timevaryingsmartdesignanddataanalysismethodsforevaluatingadaptiveinterventioneffects AT shetesanjay timevaryingsmartdesignanddataanalysismethodsforevaluatingadaptiveinterventioneffects |