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      Using classification and regression tree modelling to investigate response shift patterns in dentine hypersensitivity

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          Abstract

          Background

          Dentine hypersensitivity (DH) affects people’s quality of life (QoL). However changes in the internal meaning of QoL, known as Response shift (RS) may undermine longitudinal assessment of QoL. This study aimed to describe patterns of RS in people with DH using Classification and Regression Trees (CRT) and to explore the convergent validity of CRT with the then-test and ideals approaches.

          Methods

          Data from an 8-week clinical trial of mouthwashes for dentine hypersensitivity ( n = 75) using the Dentine Hypersensitivity Experience Questionnaire (DHEQ) as the outcome measure, were analysed. CRT was used to examine 8-week changes in DHEQ total score as a dependent variable with clinical status for DH and each DHEQ subscale score (restrictions, coping, social, emotional and identity) as independent variables. Recalibration was inferred when the clinical change was not consistent with the DHEQ change score using a minimally important difference for DHEQ of 22 points. Reprioritization was inferred by changes in the relative importance of each subscale to the model over time.

          Results

          Overall, 50.7% of participants experienced a clinical improvement in their DH after treatment and 22.7% experienced an important improvement in their quality of life. Thirty-six per cent shifted their internal standards downward and 14.7% upwards, suggesting recalibration. Reprioritization occurred over time among the social and emotional impacts of DH.

          Conclusions

          CRT was a useful method to reveal both, the types and nature of RS in people with a mild health condition and demonstrated convergent validity with design based approaches to detect RS.

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          Most cited references56

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          Integrating response shift into health-related quality of life research: a theoretical model.

          Patients confronted with a life-threatening or chronic disease are faced with the necessity to accommodate to their illness. An important mediator of this adaptation process is 'response shift' which involves changing internal standards, values and the conceptualization of quality of life (QOL). Integrating response shift into QOL research would allow a better understanding of how QOL is affected by changes in health status and would direct the development of reliable and valid measures for assessing changes in QOL. A theoretical model is proposed to clarify and predict changes in QOL as a result of the interaction of: (a) a catalyst, referring to changes in the respondent's health status; (b) antecedents, pertaining to stable or dispositional characteristics of the individual (e.g. personality); (c) mechanisms, encompassing behavioral, cognitive, or affective processes to accommodate the changes in health status (e.g. initiating social comparisons, reordering goals); and (d) response shift, defined as changes in the meaning of one's self-evaluation of QOL resulting from changes in internal standards, values, or conceptualization. A dynamic feedback loop aimed at maintaining or improving the perception of QOL is also postulated. This model is illustrated and the underlying assumptions are discussed. Future research directions are outlined that may further the investigation of response shift, by testing specific hypotheses and predictions about the QOL domains and the clinical and psychosocial conditions that would potentiate or prevent response shift effects.
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            Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research.

            The impact of health state changes on an individual's quality of life (QOL) has gained increased attention in social and medical clinical research. An emerging construct of relevance to this line of investigation is response shift phenomenon. This construct refers to the changes in internal standards, in values, or in the conceptualization of QOL which are catalyzed by health state changes. In an effort to stimulate research on response shift, we present methodological considerations and promising assessment approaches for measuring it in observational and interventional clinical research. We describe and evaluate individualized methods, preference-based methods, successive comparison methods, design approaches, statistical approaches and qualitative approaches. The hierarchical structure of the construct is also discussed, with particular emphasis on how it might be elucidated by empirical assessment which uses the proposed methods and approaches. It is also recommended that criterion measures of change be included in future studies of response shift.
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              Classification and regression tree analysis in public health: methodological review and comparison with logistic regression.

              Audience segmentation strategies are of increasing interest to public health professionals who wish to identify easily defined, mutually exclusive population subgroups whose members share similar characteristics that help determine participation in a health-related behavior as a basis for targeted interventions. Classification and regression tree (C&RT) analysis is a nonparametric decision tree methodology that has the ability to efficiently segment populations into meaningful subgroups. However, it is not commonly used in public health. This study provides a methodological overview of C&RT analysis for persons unfamiliar with the procedure. An example of a C&RT analysis is provided and interpretation of results is discussed. Results are validated with those obtained from a logistic regression model that was created to replicate the C&RT findings. Results obtained from the example C&RT analysis are also compared to those obtained from a common approach to logistic regression, the stepwise selection procedure. Issues to consider when deciding whether to use C&RT are discussed, and situations in which C&RT may and may not be beneficial are described. C&RT is a promising research tool for the identification of at-risk populations in public health research and outreach.
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                Author and article information

                Contributors
                +44 (0) 1142717877 , camachucavargas1@sheffield.ac.uk
                m.vettore@sheffield.ac.uk
                m.krasuska@sheffield.ac.uk
                s.r.baker@sheffield.ac.uk
                peter.g.robinson@bristol.ac.uk
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                14 August 2017
                14 August 2017
                2017
                : 17
                : 120
                Affiliations
                [1 ]ISNI 0000 0004 1936 9262, GRID grid.11835.3e, School of Clinical Dentistry, , University of Sheffield, ; Sheffield, UK
                [2 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, School of Oral and Dental Sciences, , University of Bristol, ; Bristol, UK
                Author information
                http://orcid.org/0000-0001-6432-5902
                Article
                396
                10.1186/s12874-017-0396-3
                5556975
                aff119fe-bd85-4350-b792-789a38001258
                © The Author(s). 2017

                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
                : 12 January 2017
                : 2 August 2017
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Medicine
                Medicine

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