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

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          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.

          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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          Most cited references26

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          Joint modelling of longitudinal measurements and event time data.

          This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.
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            An experimental design for the development of adaptive treatment strategies.

            S. Murphy (2005)
            In adaptive treatment strategies, the treatment level and type is repeatedly adjusted according to ongoing individual response. Since past treatment may have delayed effects, the development of these treatment strategies is challenging. This paper advocates the use of sequential multiple assignment randomized trials in the development of adaptive treatment strategies. Both a simple ad hoc method for ascertaining sample sizes and simple analysis methods are provided.
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              Experimental design and primary data analysis methods for comparing adaptive interventions.

              In recent years, research in the area of intervention development has been shifting from the traditional fixed-intervention approach to adaptive interventions, which allow greater individualization and adaptation of intervention options (i.e., intervention type and/or dosage) over time. Adaptive interventions are operationalized via a sequence of decision rules that specify how intervention options should be adapted to an individual's characteristics and changing needs, with the general aim to optimize the long-term effectiveness of the intervention. Here, we review adaptive interventions, discussing the potential contribution of this concept to research in the behavioral and social sciences. We then propose the sequential multiple assignment randomized trial (SMART), an experimental design useful for addressing research questions that inform the construction of high-quality adaptive interventions. To clarify the SMART approach and its advantages, we compare SMART with other experimental approaches. We also provide methods for analyzing data from SMART to address primary research questions that inform the construction of a high-quality adaptive intervention. PsycINFO Database Record (c) 2013 APA, all rights reserved
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                Author and article information

                Contributors
                tdai@mdanderson.org
                (713) 745-2483 , sshete@mdanderson.org
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                30 August 2016
                30 August 2016
                2016
                : 16
                : 1
                : 112
                Affiliations
                [1 ]Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Dr, FCT4.6002, Houston, TX 77030 USA
                [2 ]Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA
                Article
                202
                10.1186/s12874-016-0202-7
                5006275
                27578254
                58879568-9fb1-45ca-a3f2-03429ee2f9eb
                © 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.

                History
                : 19 December 2015
                : 29 July 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: R25DA026120
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01CA131324
                Award ID: P30CA016672
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000072, National Institute of Dental and Craniofacial Research;
                Award ID: R01DE022891
                Award Recipient :
                Funded by: Barnhart Family Distinguished Professorship in Targeted Therapy
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Medicine
                adaptive interventions,sequential multiple assignment randomized trial (smart),time-varying mixed effects model (tvmem),longitudinal model,joint model

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