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      suddengains: An R package to identify sudden gains in longitudinal data

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          Abstract

          Sudden gains are large and stable improvements in an outcome variable between consecutive measurements, for example during a psychological intervention with multiple assessments. Researching these occurrences could help understand individual change processes in longitudinal data. Three criteria are generally used to identify sudden gains in psychological interventions. However, applying these criteria can be time consuming and prone to errors if not fully automated. Adaptations to these criteria and methodological decisions such as how multiple gains are handled vary across studies and are reported with different levels of detail. These problems limit the comparability of individual studies and make it hard to understand or replicate the exact methods used. The R package suddengains provides a set of tools to facilitate sudden gains research. This article illustrates how to use the package to identify sudden gains or sudden losses and how to extract descriptive statistics as well as exportable data files for further analysis. It also outlines how these analyses can be customised to apply adaptations of the standard criteria. The suddengains package therefore offers significant scope to improve the efficiency, reporting, and reproducibility of sudden gains research.

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          Most cited references19

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          Missing data: our view of the state of the art.

          Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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            Clinical significance: a statistical approach to defining meaningful change in psychotherapy research.

            In 1984, Jacobson, Follette, and Revenstorf defined clinically significant change as the extent to which therapy moves someone outside the range of the dysfunctional population or within the range of the functional population. In the present article, ways of operationalizing this definition are described, and examples are used to show how clients can be categorized on the basis of this definition. A reliable change index (RC) is also proposed to determine whether the magnitude of change for a given client is statistically reliable. The inclusion of the RC leads to a twofold criterion for clinically significant change.
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              Sudden gains and critical sessions in cognitive-behavioral therapy for depression.

              In this study of cognitive-behavioral therapy for depression, many patients experienced large symptom improvements in a single between-sessions interval. These sudden gains' average magnitude was 11 Beck Depression Inventory points, accounting for 50% of these patients' total improvement. Patients who experienced sudden gains were less depressed than the other patients at posttreatment, and they remained so 18 months later. Substantial cognitive changes were observed in the therapy sessions preceding sudden gains, but few cognitive changes were observed in control sessions, suggesting that cognitive change in the pregain sessions triggered the sudden gains. Improved therapeutic alliances were also observed in the therapy sessions immediately after the sudden gains, as were additional cognitive changes, suggesting a three-stage model for these patients' recovery: preparation-->critical session/sudden gain-->upward spiral.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2020
                9 March 2020
                : 15
                : 3
                : e0230276
                Affiliations
                [1 ] Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
                [2 ] Oxford Health NHS Foundation Trust, Oxford, United Kingdom
                [3 ] Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
                [4 ] King’s College London, London, United Kingdom
                Leibniz Institute for Educational Trajectories, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1991-282X
                http://orcid.org/0000-0003-2851-1315
                http://orcid.org/0000-0002-8742-0192
                Article
                PONE-D-19-30704
                10.1371/journal.pone.0230276
                7062272
                32150589
                79f9daf6-17c4-4fce-adfc-d775e4abb130
                © 2020 Wiedemann et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 November 2019
                : 25 February 2020
                Page count
                Figures: 2, Tables: 4, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100009981, Mental Health Research UK;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 102176
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: 069777 and 200976
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100013373, NIHR Oxford Biomedical Research Centre;
                Award Recipient :
                Funded by: Oxford Health NIHR Biomedical Research Centre
                Award Recipient :
                This project was supported by a Mental Health Research UK studentship (MW), the Wellcome Trust [102176 (GRT); 069777 and 200976 (AE, RS)], the Oxford Health NIHR Biomedical Research Centre (MW, GRT, AE), and the NIHR Oxford Biomedical Research Centre (GRT). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mental Health Therapies
                Research and Analysis Methods
                Research Assessment
                Reproducibility
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Depression
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Research and Analysis Methods
                Research Assessment
                Research Validity
                Computer and Information Sciences
                Information Technology
                Data Processing
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mental Health Therapies
                Psychotherapy
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Metaanalysis
                Custom metadata
                The package can be downloaded from CRAN https://CRAN.R-project.org/package=suddengains. All code, materials, and data can be found at https://github.com/milanwiedemann/suddengains. Instructions for installing the package, further technical details, and examples can be found at https://milanwiedemann.github.io/suddengains. The R code examples in this paper refer to package version 0.4.0.

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