Proper Multiple Imputation of Clustered or Panel Data

Spieß, Martin; Kleinke, Kristian; Reinecke, Jost

doi:10.1002/9781119376965.ch17

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…We applied Multiple Imputation (Rubin, 1987) for missing BAI and BDI-II values to minimize bias due to missing values during hypotheses testing. We used fully conditional specification and a two-level imputation model for longitudinal data, including all model-relevant variables and their interaction terms as well as auxiliary variables with a correlation of at least r ≥ .2 with either the imputed variable or missingness (Kleinke, Reinecke, Salfrán, & Spiess, 2020;Spiess, Kleinke, & Reinecke, 2021;van Buuren, 2018). Fifty imputed data sets were generated with 20 iterations each (White, Royston, & Wood, 2011).…”

Section: Discussionmentioning

confidence: 99%

Trauma History and Course of Therapy in a Naturalistic Cognitive Behavior Therapy Outpatient Sample: An Archive Data Study

Epe-Jungeblodt,

Pauli,

Schwab

et al. 2023

Preprint

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Objective: Cognitive Behavior Therapy (CBT) is an effective treatment for anxiety and depression disorders. Nonetheless, nearly 50% of all patients do not respond. Besides other factors, this seems to be linked to the experience of traumatic life events. This study aims to assess the effects of trauma history on the choice of therapy interventions and treatment outcomes. Methods: We analyzed data from 340 CBT outpatients diagnosed with a depression or anxiety disorder and possibly a trauma history treated under naturalistic conditions. Based on their written therapy files, we collected information on trauma history, psychiatric diagnoses, duration of therapy, applied interventions, and severity of depression and anxiety symptoms at the start and end of therapy. The influence of trauma, diagnoses, and intervention use on the development of depression and anxiety symptoms were analyzed. Results: Patients with a trauma history reported higher overall depression and anxiety symptoms than those without trauma. No differences in the duration of therapy, applied interventions, or decrease in symptom severity were found between patients with and without a trauma history. Trauma-specific interventions failed to boost treatment success; however, they were also seldom applied. Conclusion: Although no differences between traumatized and non-traumatized patients were found for naturalistic CBT, traumatized patients maintained higher levels of symptom severity irrespective of diagnoses. These results indicate a need for more trauma-specific and personalized interventions. Therapists may need evidence-based guidelines to personalize CBT for patients with a trauma history and high symptom severity.

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Section: Discussionmentioning

confidence: 99%

Trauma History and Course of Therapy in a Naturalistic Cognitive Behavior Therapy Outpatient Sample: An Archive Data Study

Epe-Jungeblodt,

Pauli,

Schwab

et al. 2023

Preprint

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Exploring Impact of Marijuana (Cannabis) Abuse on Adults Using Machine Learning

Choi

Chung

Choi

2021

IJERPH

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Marijuana is the most common illicit substance globally. The rate of marijuana use is increasing in young adults in the US. The current environment of legalizing marijuana use is further contributing to an increase of users. The purpose of this study was to explore the characteristics of adults who abuse marijuana (20–49 years old) and analyze behavior and social relation variables related to depression and suicide risk using machine-learning algorithms. A total of 698 participants were identified from the 2019 National Survey on Drug Use and Health survey as marijuana dependent in the previous year. Principal Component Analysis and Chi-square were used to select features (variables) and mean imputation method was applied for missing data. Logistic regression, Random Forest, and K-Nearest Neighbor machine-learning algorithms were used to build depression and suicide risk prediction models. The results showed unique characteristics of the group and well-performing prediction models with influential risk variables. Identified risk variables were aligned with previous studies and suggested the development of marijuana abuse prevention programs targeting 20–29 year olds with a regular depression and suicide screening. Further study is suggested for identifying specific barriers to receiving timely treatment for depression and suicide risk.

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Proper Multiple Imputation of Clustered or Panel Data

Cited by 2 publications

References 37 publications

Trauma History and Course of Therapy in a Naturalistic Cognitive Behavior Therapy Outpatient Sample: An Archive Data Study

Trauma History and Course of Therapy in a Naturalistic Cognitive Behavior Therapy Outpatient Sample: An Archive Data Study

Exploring Impact of Marijuana (Cannabis) Abuse on Adults Using Machine Learning

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