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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2019)

Over the last few decades, a large body of research supports the use of multiple imputation as a method for handling missing data. Despite imputation’s broad appeal, the method is known to introduce biases when applied to models with interactive and polynomial effects. In the context of single-level regression models, multiple imputation based on a fully Bayesian model specification has shown great promise, but limited research to date has considered this approach for multilevel models. The purpose of this dissertation is to investigate the multilevel extension of Bayesian model-based imputation to a two-level regression model with a cross-level interactive effect.With the exception of some rather extreme scenarios with non- normal data, computer simulations from this research suggest that the model-based approach can effectively estimate these models in a wide variety of conditions that are typical of social and behavioral science research data. In virtually every condition examined, model-based imputation outperformed existing alternatives to handling incomplete interactive effects. This procedure is available in the Blimp software package for macOS, Windows, and Linux.

    Cover page: Model-Based Imputation for Multilevel Interaction Effects
    • Thesis
    • Peer Reviewed
    UCLA Electronic Theses and Dissertations (2023)

    This study investigates the effectiveness of Bayesian variable selection (BVS) procedures in dealing with missing not at random (MNAR) data for identification in selection models. Three BVS-adapted selection models, namely Bayesian LASSO, horseshoe prior, and spike-and-slab prior, were compared, along with established missing data methods such as a model that assumes a missing at random (MAR) process and full-selection model. The results indicate that the spike-and-slab prior consistently outperformed other BVS methods in terms of accuracy and bias for various parameters, including slope estimates, residual variance, and intercept. When compared with the full-selection model, the spike-and-slab model exhibited superior performance across all parameters based on mean squared error (MSE) results.Although the MAR and spike-and-slab models showed comparable performance for slope estimates, the spike-and-slab model consistently outperformed the MAR model in estimating residual variance and intercept. This comparable performance is attributed to the bias-variance tradeoff. The MAR model, while biased, demonstrated efficiency by estimating fewer parameters than selection models and obtaining robust support from the observed data. On the other hand, the spike-and-slab model outperformed the full-selection model, even when the full-selection model aligned with the true data-generating model. The adaptation of BVS to selection models, particularly through the spike-and-slab method, yielded promising results with unbiased estimates under various conditions. However, it is important to acknowledge that this study represents an initial exploration of this subject, and its scope was inherently limited. Finally, the BVS adaptations to the selection model was illustrated with data from a clinical-trial study.

      Cover page: Bayesian Selection Model with Shrinking Priors for Nonignorable Missingness
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2021)

      Background

      Stressful life experiences and personality can influence one another. Personality may contribute to the amount and type of stress individuals experience, which is referred to as a selection effect. Life stress may also impact one's personality, which is referred to as a socialization effect. It was hypothesized that neuroticism would predict increased chronic and episodic stress (selection effect) and that chronic and episodic stress would predict increased neuroticism (socialization effect).

      Methods

      The current study investigated selection and socialization effects of neuroticism and life stress over a three-year period in 627 adolescents. Life stress data were examined in terms of duration (chronic versus episodic) and type (interpersonal versus non-interpersonal). Episodic stress data were examined as dependent or independent.

      Results

      The results from ten cross-lagged panel models provided some evidence for significant selection and socialization effects depending on stress type. Over three years, we observed that neuroticism increases interpersonal chronic stress and non-interpersonal stressful events (selection effects) and that dependent non-interpersonal stressful events and chronic stress increase neuroticism (socialization effects).

      Limitations

      Study limitations include a lack of a lifespan perspective and a statistical approach that does not differentiate between- from within-person variance.

      Conclusions

      Findings suggest the value of attending to stress response as well as targeting neuroticism in prevention and intervention approaches in adolescents.
        Cover page: Reciprocal effects of neuroticism and life stress in adolescence
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2022)
        Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method can handle missingness appropriately. Computer simulation results suggested that the proposed Bayesian latent variable selection model (BLVSM) was quite effective when the outcome and/or covariates were MNAR. Except when the sample size was small, estimates from the proposed BLVSM tracked closely with those from the complete data analysis. With a small sample size, when the outcome was less predictable from the covariates, the missingness proportions of the covariates and the outcome were larger, and the missingness selection processes of the covariates and the outcome were more MNAR and MAR, the performance of BLVSM was less satisfactory. When the sample size was large, BLVSM always performed well. In contrast, the method with an MAR assumption provided biased estimates and undercoverage confidence intervals when the missingness was MNAR. The robustness and the implementation of BLVSM in real data were also illustrated. The proposed method is available in the Blimp software application, and the paper includes a data analysis example illustrating its use.
          Cover page: A Bayesian Latent Variable Selection Model for Nonignorable Missingness
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2022)
          Although it has been demonstrated that (a) body dissatisfaction and internalization of societal appearance standards contribute to disordered eating and (b) that internalization of societal appearance standards leads to decreased skin color satisfaction among Black women, it has not been established whether skin color dissatisfaction contributes to disordered eating among Black women or girls. The objective of the present study is to determine the influence of skin color satisfaction as a potential predictor for binge eating, and its effect through body image in Black girls during the vulnerable developmental period of adolescence. Using data from ten annual measurements in 1213 Black girls across ages 10-19, we sought to determine whether skin color satisfaction predicts Binge Eating Disorder (BED) risk and symptoms using pre-registered logistic and multilevel models. We found that lower skin color satisfaction at ages 13 and 14 significantly predicted greater odds of BED and lower skin color satisfaction at all ages predicted greater BED symptoms. Body satisfaction mediated the relationship between skin color satisfaction and BED symptoms. Our results suggest that skin color dissatisfaction is a novel component of body image for Black girls that is also related to binge eating.
            Cover page: Prospective relationships between skin color satisfaction, body satisfaction, and binge eating in Black girls
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2023)
            The purpose of the current study was to test the longitudinal association between disordered eating symptoms (body dissatisfaction, drive for thinness and bulimia) in adolescence (ages 12, 14, 16, 18, 19) and adulthood (age 40) in a sample of 883 white and Black women. We also investigated moderation by race. Adolescent symptoms at each time point significantly predicted adulthood symptoms for the body dissatisfaction and drive for thinness subscales, for both Black and white women. Bulimia symptoms in adolescence predicted symptoms in adulthood; however, the effect was largely driven by white women. Although moderation was non-significant, among white women, bulimia symptoms at all adolescent time points predicted adulthood bulimia, but among Black women, only symptoms at ages 18 and 19 were predictive of adulthood bulimia. Results suggest that both Black and white women are susceptible to disordered eating and that symptoms emerging in adolescence can potentially follow women into midlife.
              Creative Commons 'BY-NC' version 4.0 license
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2021)

              Objective

              Pharmacological treatments that can concomitantly address cigarette smoking and heavy drinking stand to improve health care delivery for these highly prevalent co-occurring conditions. This superiority trial compared the combination of varenicline and naltrexone against varenicline alone for smoking cessation and drinking reduction among heavy-drinking smokers.

              Methods

              This was a phase 2 randomized double-blind clinical trial. Participants (N=165) who were daily smokers and drank heavily received either 2 mg/day of varenicline plus 50 mg/day of naltrexone or 2 mg/day of varenicline plus matched placebo pills for 12 weeks. Primary outcomes were 7-day point prevalence of nicotine abstinence (bioverified by a breath CO reading ≤5 ppm) at the 26-week follow-up and number of drinks per drinking day during the 12-week treatment phase.

              Results

              Smoking abstinence at week 26 was significantly higher in the varenicline plus placebo condition than in the varenicline plus naltrexone condition (N=37 [45.1%] compared with N=22 [26.5%]). For drinks per drinking day, there was a medication effect favoring the combination of varenicline and naltrexone over varenicline alone across the 12-week treatment phase, although it did not meet the significance threshold.

              Conclusions

              These findings suggest that smoking cessation and drinking reduction can be concomitantly targeted with pharmacotherapy and that while varenicline alone may be sufficient as a smoking cessation aid in heavy-drinking smokers, the combination of varenicline and naltrexone may confer benefits with regard to drinking outcomes, particularly during the 12-week period of active medication treatment.
                Cover page: Efficacy of Combining Varenicline and Naltrexone for Smoking Cessation and Drinking Reduction: A Randomized Clinical Trial
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