Design and evaluation of the parent education component of a comprehensive violence prevention project in middle schools

Low parental monitoring is related to youth risk behaviors such as delinquency and aggression. The purpose of this dissertation was to describe the development and evaluation of a parent education intervention to increase parental monitoring in Hispanic parents of middle school children.^ The first study described the process of intervention mapping as used to develop Padres Trabajando por la Paz, a newsletter intervention for parents. Using theory, empirical literature, and information from the target population, performance objectives and determinants for monitoring were defined. Learning objectives were specified and a staged social-cognitive approach was used to develop methods and strategies delivered through newsletters.^ The second study examined the outcomes of a randomized trial of the newsletter intervention. Outcome measures consisted of a general measure of monitoring, parent and child reports of monitoring behaviors targeted by the intervention, and psychosocial determinants of monitoring (self-efficacy, norms, outcome expectancies, knowledge, and beliefs). Seventy-seven parents completed the randomized trial, half of which received four newsletters over an eight-week period. Results revealed a significant interaction effect for baseline and treatment for parent's reports of norms for monitoring (p =.009). Parents in the experimental condition who scored low at baseline reported increased norms for monitoring at follow-up. A significant interaction effect for child reports of parental monitoring behaviors (p =.04) reflected an small increase across baseline levels in the experimental condition and decreases for the control condition at higher baseline scores. Both groups of parents reported increased levels of monitoring at follow-up. No other outcome measures varied significantly by condition.^ The third study examined the relationship between the psychosocial determinants of parental monitoring and parental monitoring behaviors in the study population. Weak evidence for a relationship between outcome expectancies and parental monitoring behaviors suggests further research in the area utilizing stronger empirical models such as longitudinal design and structural equation modeling.^ The low-cost, minimal newsletter intervention showed promise for changing norms among Hispanic parents for parental monitoring. In light of the importance of parental monitoring as a protective factor for youth health risk behaviors, more research needs to be done to develop and evaluate interventions to increase parental monitoring. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^