The Bradley–Terry Regression Trunk approach for Modeling Preference Data with Small Trees

This paper introduces the Bradley-Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects. We, therefore, combine a tree-based model and the log-linear Bradley-Terry model using the outcome of the comparisons as response variable. The proposed model provides a solution to discover interaction effects when no a-priori hypotheses are available. It produces a small tree, called trunk, that represents a fair compromise between a simple interpretation of the interaction effects and an easy to read partition of judges based on their characteristics and the preferences they have expressed. We present an application on a real dataset following two different approaches, and a simulation study to test the model's performance. Simulations showed that the quality of the model performance increases when the number of rankings and objects increases. In addition, the performance is considerably amplified when the judges' characteristics have a high impact on their choices

Do automated digital health behaviour change interventions have a positive effect on self-efficacy? A systematic review and meta-analysis

© 2019 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Health Psychology Review on 20/01/2020, available online: https://doi.org/10.1080/17437199.2019.1705873.Self-efficacy is an important determinant of health behaviour. Digital interventions are a potentially acceptable and cost-effective way of delivering programmes of health behaviour change at scale. Whether behaviour change interventions work to increase self-efficacy in this context is unknown. This systematic review and meta-analysis sought to identify whether automated digital interventions are associated with positive changes in self-efficacy amongst non-clinical populations for five major health behaviours, and which BCTs are associated with that change. A systematic literature search identified 20 studies (n=5624) that assessed changes in self-efficacy and were included in a random effects meta-analysis. Interventions targeted: healthy eating (k=4), physical activity (k=9), sexual behaviour (k=3), and smoking (k=4). No interventions targeting alcohol use were identified. Overall, interventions had a small, positive effect on self-efficacy (푔 = 0.190, CI [0.078; 0.303]). The effect of interventions on self-efficacy did not differ as a function of health behaviour type (Qbetween = 7.3704 p = 0.061, df = 3). Inclusion of the BCT ‘information about social and environmental consequences’ had a small, negative effect on self-efficacy (Δ푔= - 0.297, Q=7.072, p=0.008). Whilst this review indicates that digital interventions can be used to change self-efficacy, which techniques work best in this context is not clear.Peer reviewedFinal Accepted Versio

Measuring determinants of implementation behavior: psychometric properties of a questionnaire based on the theoretical domains framework

BACKGROUND: To be able to design effective strategies to improve healthcare professionals’ implementation behaviors, a valid and reliable questionnaire is needed to assess potential implementation determinants. The present study describes the development of the Determinants of Implementation Behavior Questionnaire (DIBQ) and investigates the reliability and validity of this Theoretical Domains Framework (TDF)-based questionnaire. METHODS: The DIBQ was developed to measure the potential behavioral determinants of the 12-domain version of the TDF (Michie et al., 2005). We identified existing questionnaires including items assessing constructs within TDF domains and developed new items where needed. Confirmatory factor analysis was used to examine whether the predefined structure of the TDF-based questionnaire was supported by the data. Cronbach’s alpha was calculated to assess internal consistency reliability of the questionnaire, and domains’ discriminant validity was investigated. RESULTS: We developed an initial questionnaire containing 100 items assessing 12 domains. Results obtained from confirmatory factor analysis and Cronbach’s alpha resulted in the final questionnaire consisting of 93 items assessing 18 domains, explaining 63.3% of the variance, and internal consistency reliability values ranging from .68 to .93. Domains demonstrated good discriminant validity, although the domains ‘Knowledge’ and ‘Skills’ and the domains ‘Skills’ and ‘Social/professional role and identity’ were highly correlated. CONCLUSIONS: We have developed a valid and reliable questionnaire that can be used to assess potential determinants of healthcare professional implementation behavior following the theoretical domains of the TDF. The DIBQ can be used by researchers and practitioners who are interested in identifying determinants of implementation behaviors in order to be able to develop effective strategies to improve healthcare professionals’ implementation behaviors. Furthermore, the findings provide a novel validation of the TDF and indicate that the domain ‘Environmental context and resources’ might be divided into several environment-related domains

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

Effects of a cognitive behavioral self-help program and a computerized structured writing intervention on depressed mood for HIV-infected people:A pilot randomized controlled trial

Objective: The aim of the present study was to examine whether low-resource, cost-effective intervention programs can be effective in improving depressed mood in people with HIV. The efficacy of a cognitive-behavioral self-help program (CBS) and a computerized structured writing intervention (SWI) were tested in a pilot randomized controlled trial. Methods: Participants were members of a patient organization. They completed a pretest and posttest. The questionnaire included the HADS. Participants were randomly allocated to CBS (n=24), SWI (n=25) or a waiting list condition (WLC, n=24). To evaluate changes in the continuous outcome measure, a 3 × 2 (group × time) repeated measures ANCOVA was performed. Also, an ANCOVA was performed using change scores. Results: Respondents who followed the CBS improved significantly compared to the WLC. However, for people in the SWI condition no significant improvement on depression was found. Conclusion: This pilot study suggests that a low-resource, cost-effective CBS program seems to be effective in reducing depressed mood in people living with HIV. Practice implications: Because self-help programs can be delivered through regular mail or the internet, a high number of people could be reached while overcoming geographical and social barriers to treatment. © 2009 Elsevier Ireland Ltd

Interventions to Promote Healthy Eating, Physical Activity and Smoking in Low-Income Groups: a Systematic Review with Meta-Analysis of Behavior Change Techniques and Delivery/Context

Purpose Healthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults. Method Data from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions. Results For healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with selfmonitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking. Conclusion This study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities