Moderators of two Indicated Cognitive-Behavioral Depression Prevention Approaches for Adolescents in a School-Based Effectiveness Trial

Objective: Our aim was to identify moderators of the effects of a cognitive behavioral group-based prevention program (CB group) and CB bibliotherapy, relative to an educational brochure control condition and to one another, in a school-based effectiveness randomized controlled prevention trial. Method: 378 adolescents (M age ¼ 15.5, 68% female) with elevated depressive symptoms were randomized in one of three conditions and were assessed at pretest, posttest, and 6-month follow-up. We tested the moderating effect of three individual (baseline depressive symptoms, negative attributional style, substance use), three environmental (negative life events, parental support, peer support), and two sociodemographic (sex, age) characteristics. Results: Baseline depressive symptoms interacted with condition and time. Decomposition indicated that elevated baseline depressive symptoms amplified the effect of CB bibliotherapy at posttest (but not 6-month follow-up) relative to the control condition, but did not modify the effect of CB group relative to the control condition or relative to bibliotherapy. Specifically, CB bibliotherapy resulted in lower posttest depressive symptoms than the control condition in individuals with elevated, but not average or low baseline symptoms. We found no interaction effect for other putative moderators. Conclusions: Our findings suggest that bibliotherapy is effective only in participants who have elevated depressive symptoms at baseline. The fact that no study variable moderated the effects of CB group, which had a significant main effect in reducing depressive symptoms relative to the control condition, suggests that this indicated prevention intervention is effective for a wide range of adolescents.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.