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      Testing the Feasibility of a Guided Imagery Tobacco Cessation Intervention Delivered by a Telephone Quitline: Study Protocol for a Randomized Controlled Feasibility Trial

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      Contemporary Clinical Trials Communications
      Elsevier BV

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          Abstract

          Background Guided imagery (GI) is an evidence-based method that uses the imagination to practice and achieve a desired outcome. Little research has focused on how GI can be delivered to smokers using remote or virtual methods, such as a telephone-based intervention. Telephone-based services for tobacco cessation (quitlines) have emerged as standard of care for tobacco cessation. However, quitlines reach only a small fraction of smokers, and men and racial/ethnic minorities are less likely to use quitlines than majority women. GI has the potential to attract under-served minority groups as well as smokers who are looking for an alternative approach to cessation. The present study is designed to test the feasibility and potential impact of a GI tobacco cessation intervention delivered by telephone. This study compares the GI intervention with a standard behavioral (SB) intervention. Methods Participants (N = 100) are randomized to either the GI (intervention) or SB (control) condition. Each condition features a 6-week intervention in which participants work with coaches to quit tobacco. Primary outcomes are feasibility related (recruitment, retention, adherence), and secondary outcomes include cessation at 6 months post-intervention (7-day and 30-day abstinence). Discussion A GI intervention delivered via quitline would allow for scalability and dissemination, potentially reaching a more representative group of smokers. Results from this study will determine the feasibility of delivering the GI intervention, and describe the reach of the intervention to under-represented tobacco users. If successful, our study results will guide the design and conduct of a future efficacy trial.

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              Practical and statistical issues in missing data for longitudinal patient-reported outcomes.

              Patient-reported outcomes are increasingly used in health research, including randomized controlled trials and observational studies. However, the validity of results in longitudinal studies can crucially hinge on the handling of missing data. This paper considers the issues of missing data at each stage of research. Practical strategies for minimizing missingness through careful study design and conduct are given. Statistical approaches that are commonly used, but should be avoided, are discussed, including how these methods can yield biased and misleading results. Methods that are valid for data which are missing at random are outlined, including maximum likelihood methods, multiple imputation and extensions to generalized estimating equations: weighted generalized estimating equations, generalized estimating equations with multiple imputation, and doubly robust generalized estimating equations. Finally, we discuss the importance of sensitivity analyses, including the role of missing not at random models, such as pattern mixture, selection, and shared parameter models. We demonstrate many of these concepts with data from a randomized controlled clinical trial on renal cancer patients, and show that the results are dependent on missingness assumptions and the statistical approach. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
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                Author and article information

                Journal
                Contemporary Clinical Trials Communications
                Contemporary Clinical Trials Communications
                Elsevier BV
                24518654
                August 2019
                August 2019
                : 100437
                Article
                10.1016/j.conctc.2019.100437
                2911a473-5e68-4957-8afc-74b363e041c7
                © 2019

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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