Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
Abstract 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 st...
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| Format: | Article |
| Language: | English |
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BMC
2016-08-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | http://link.springer.com/article/10.1186/s12874-016-0202-7 |
| _version_ | 1818050148091035648 |
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| author | Tianjiao Dai Sanjay Shete |
| author_facet | Tianjiao Dai Sanjay Shete |
| author_sort | Tianjiao Dai |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-10T10:48:52Z |
| format | Article |
| id | doaj.art-290cea523bfb4879b12a7ac3d4d923af |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-10T10:48:52Z |
| publishDate | 2016-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-290cea523bfb4879b12a7ac3d4d923af2022-12-22T01:52:04ZengBMCBMC Medical Research Methodology1471-22882016-08-0116111710.1186/s12874-016-0202-7Time-varying SMART design and data analysis methods for evaluating adaptive intervention effectsTianjiao Dai0Sanjay Shete1Department of Biostatistics, The University of Texas MD Anderson Cancer CenterDepartment of Biostatistics, The University of Texas MD Anderson Cancer CenterAbstract 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.http://link.springer.com/article/10.1186/s12874-016-0202-7Adaptive interventionsSequential multiple assignment randomized trial (SMART)Time-varying mixed effects model (TVMEM)Longitudinal modelJoint model |
| spellingShingle | Tianjiao Dai Sanjay Shete Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects BMC Medical Research Methodology Adaptive interventions Sequential multiple assignment randomized trial (SMART) Time-varying mixed effects model (TVMEM) Longitudinal model Joint model |
| 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 | Adaptive interventions Sequential multiple assignment randomized trial (SMART) Time-varying mixed effects model (TVMEM) Longitudinal model Joint model |
| url | http://link.springer.com/article/10.1186/s12874-016-0202-7 |
| work_keys_str_mv | AT tianjiaodai timevaryingsmartdesignanddataanalysismethodsforevaluatingadaptiveinterventioneffects AT sanjayshete timevaryingsmartdesignanddataanalysismethodsforevaluatingadaptiveinterventioneffects |