Sequential Bayesian Data Synthesis for Mediation and Regression Analysis.

Science is an inherently cumulative process, and knowledge on a specific topic is organized through synthesis of findings from related studies. Meta-analysis has been the most common statistical method for synthesizing findings from multiple studies in prevention science and other fields. In recent years, Bayesian statistics have been put forth as another way to synthesize findings and have been praised for providing a natural framework for update existing knowledge with new data. This article presents a Bayesian method for cumulative science and describes a SAS macro %SBDS for synthesizing findings from multiple studies or multiple data sets from a single study using three different methods: meta-analysis using raw data, sequential Bayesian data synthesis, and a single-level analysis on pooled data. Sequential Bayesian data synthesis and Bayesian statistics in general are discussed in an accessible manner, and guidelines are provided on how researchers can use the accompanying SAS macro for synthesizing data from their own studies. Four alcohol use studies were used to demonstrate how to apply the three data synthesis methods using the SAS macro.

Self-regulation interventions to reduce consumption of sugar-sweetened beverages in adolescents.

This study evaluated the efficacy of self-regulation interventions through the use of drink-specific implementation intentions and drink-specific Go/No-Go training tasks as compensatory strategies to modify inhibitory control to reduce intake of sugar-sweetened beverages (SSB). In a between-subjects randomized manipulation of implementation intentions and Go/No-Go training to learn to inhibit sugary drink consumption, 168 adolescents reporting inhibitory control problems over sugary drinks and foods were recruited from high schools in southern California to participate. Analysis of covariance overall test of effects revealed no significant differences between the groups regarding calories consumed, calories from SSBs, grams of sugar consumed from drinks, or the number of unhealthy drinks chosen. However, subsequent contrasts revealed SSB implementation intentions significantly reduced SSB consumption following intervention while controlling for inhibitory control failure and general SSB consumption during observation in a lab setting that provided SSBs and healthy drinks, as well as healthy and unhealthy snacks. Specifically, during post-intervention observation, participants in the sugar-sweetened beverage implementation intentions (SSB-II) conditions consumed significantly fewer calories overall, fewer calories from drinks, and fewer grams of sugar. No effects were found for the drink-specific Go/No-Go training on SSB or calorie consumption. However, participants in SSB-II with an added SSB Go/No-Go training made fewer unhealthy drink choices than those in the other conditions. Implementation intentions may aid individuals with inhibitory (executive control) difficulties by intervening on pre-potent behavioral tendencies, like SSB consumption.

The Influence of Brief Outing and Temporary Fostering Programs on Shelter Dog Welfare.

Human interaction is one of the most consistently effective interventions that can improve the welfare of shelter-living dogs. Time out of the kennel with a person has been shown to reduce physiological measures of stress as can leaving the shelter for a night or more in a foster home. In this study, we assessed the effects of brief outings and temporary fostering stays on dogs' length of stay and outcomes. In total, we analyzed data of 1955 dogs from 51 animal shelters that received these interventions as well as 25,946 dogs residing at these shelters that served as our controls. We found that brief outings and temporary fostering stays increased dogs' likelihood of adoption by 5.0 and 14.3 times, respectively. While their lengths of stay were longer in comparison to control dogs, this difference was present prior to the intervention. Additionally, we found that these programs were more successful when greater percentages of community members (as compared to volunteers and staff) were involved in caregiving as well as when programs were implemented by better-resourced shelters. As such, animal welfare organizations should consider implementing these fostering programs as evidence-based best practices that can positively impact the outcomes of shelter dogs.

Estimating and Testing Causal Mediation Effects in Single-Case Experimental Designs Using State-Space Modeling.

In this article, we present single-case causal mediation analysis as the application of causal mediation analysis to data collected within a single-case experiment. This method combines the focus on the individual with the focus on mechanisms of change, rendering it a promising approach for both mediation and single-case researchers. For this purpose, we propose a new method based on time-discrete state-space modeling to estimate the direct and indirect treatment effects. We demonstrate how to estimate the model for a single-case experiment on stress and craving in a routine alcohol consumer before and after an imposed period of abstinence. Furthermore, we present a simulation study that examines the estimation and testing of the standardized indirect effect. All parameters used to generate the data were recovered with acceptable precision. We use maximum likelihood and permutation procedures to calculate p-values and standard errors of the parameters estimates. The new method is promising for testing mediated effects in single-case experimental designs. We further discuss limitations of the new method with respect to causal inference, as well as more technical concerns, such as the choice of the time lags between the measurements.

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study.

The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, small, medium, large) in a Monte Carlo simulation study of 2- and 3-class models. The results suggested that in general, a larger sample size, more indicators, a higher quality of indicators, and a larger covariate effect lead to more converged and proper replications, as well as fewer boundary parameter estimates and less parameter bias. Furthermore, interactions among these study factors demonstrated how using more or higher quality indicators, as well as larger covariate effect size, could sometimes compensate for small sample size. Including a covariate appeared to be generally beneficial, although the covariate parameters themselves showed relatively large bias. Our results provide useful information for practitioners designing an LCA study in terms of highlighting the factors that lead to better or worse performance of LCA.