Dataset Information

Assessing the collective utility of multiple analyses on clinical alcohol use disorder data.

ABSTRACT: OBJECTIVE:The objective of this study was to assess the potential of combining graph learning methods with latent variable estimation methods for mining clinically useful information from observational clinical data sets. MATERIALS AND METHODS:The data set contained self-reported measures of psychopathology symptoms from a clinical sample receiving treatment for alcohol use disorder. We used the traditional graph learning methods: Graphical Least Absolute Shrinkage and Selection Operator, and Friedman's hill climbing algorithm; traditional latent variable estimation method factor analysis; recently developed graph learning method Greedy Fast Causal Inference; and recently developed latent variable estimation method Find One Factor Clusters. Methods were assessed qualitatively by the content of their findings. RESULTS:Recently developed graphical methods identified potential latent variables (ie, not represented in the model) influencing particular scores. Recently developed latent effect estimation methods identified plausible cross-score loadings that were not found with factor analysis. A graphical analysis of individual items identified a mistake in wording on 1 questionnaire and provided further evidence that certain scores are not reflective of indirectly measured common causes. DISCUSSION AND CONCLUSION:Our findings suggest that a combination of Greedy Fast Causal Inference and Find One Factor Clusters can enhance the evidence-based information yield from psychopathological constructs and questionnaires. Traditional methods provided some of the same information but missed other important findings. These conclusions point the way toward more informative interrogations of existing and future data sets than are commonly employed at present.

SUBMITTER: Kummerfeld E

PROVIDER: S-EPMC6748805 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Assessing the collective utility of multiple analyses on clinical alcohol use disorder data.

Kummerfeld Erich E Rix Alexander A Anker Justin J JJ Kushner Matt G MG

Journal of the American Medical Informatics Association : JAMIA 20191001 10

<h4>Objective</h4>The objective of this study was to assess the potential of combining graph learning methods with latent variable estimation methods for mining clinically useful information from observational clinical data sets.<h4>Materials and methods</h4>The data set contained self-reported measures of psychopathology symptoms from a clinical sample receiving treatment for alcohol use disorder. We used the traditional graph learning methods: Graphical Least Absolute Shrinkage and Selection O ...[more]

PMID: 30990526

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Project description:BackgroundSelf-efficacy has been proposed as a key predictor of alcohol treatment outcomes and a potential mechanism of success in achieving abstinence or drinking reductions following alcohol treatment. Integrative data analysis, where data from multiple studies are combined for analyses, can be used to synthesize analyses across multiple alcohol treatment trials by creating a commensurate measure and controlling for differential item functioning (DIF) to determine whether alcohol treatments improve self-efficacy.MethodThe current study used moderated nonlinear factor analysis (MNLFA) to examine the effect of treatment on self-efficacy across four different treatment studies (N = 3720; 72.5% male, 68.4% non-Hispanic white). Self-efficacy was measured using the Alcohol Abstinence Self-Efficacy Scale (AASE) in the COMBINE Study (n = 1383) and Project MATCH (n = 1726), and the Drug Taking Confidence Questionnaire (DTCQ) in two studies of Telephone Continuing Care (TEL Study 1: n = 303; TEL Study 2: n = 212). DIF was examined across time, study, treatment condition, marital status, age, and sex.ResultsWe identified 12 items from the AASE and DTCQ to create a commensurate measure of self-efficacy using MNLFA. All active treatments, including cognitive-behavioral treatment, a combined behavioral intervention, medication management, motivation enhancement treatment, telephone continuing care, twelve-step facilitation, and relapse prevention, were associated with significant increases in self-efficacy from baseline to posttreatment that were maintained for up to a year. Importantly, treatment as usual in community settings, which consisted of weekly group therapy that included addiction counseling and twelve-step recovery support, was not associated with significant increases in self-efficacy.ConclusionsAlcohol self-efficacy increases following treatment and numerous evidence-based treatments are associated with significant increases in self-efficacy, which are maintained over time. Community treatment that focuses solely on addiction counseling and twelve-step support may not promote increases in self-efficacy.

Dataset Information

Assessing the collective utility of multiple analyses on clinical alcohol use disorder data.

Publications

Assessing the collective utility of multiple analyses on clinical alcohol use disorder data.

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