Buying a pig in a poke: An experimental study of unconditional veto power

We study an ultimatum experiment in which the responder does not know the offer when accepting or rejecting. Unconditional veto power leads to acceptances, although proposers are significantly greedier than in standard ultimatum games, and this is anticipated by responders. We also elicit responders’ willingness to pay for (un)conditional veto power. The bids reveal a large endowment effect.     

Fat in the Fire? Science,the News Media,and the “Obesity Epidemic”2

In recent years, the “obesity epidemic” has emerged as a putative public health crisis. This article examines the interconnected role of medical science and news reporting in shaping the way obesity is framed as a social problem. Drawing on a sample of scientific publications on weight and health, and press releases and news reporting on these publications, we compare and contrast social problem frames in medical science and news reporting. We find substantial overlap in science and news reporting, but the news media do dramatize more than the studies on which they are reporting and are more likely than the original science to highlight individual blame for weight. This is partly due to the news media’s tendency to report more heavily on the most alarmist and individual‐blaming scientific studies. We find some evidence that press releases also shape which articles receive media coverage and how they are framed.     

Applications of Bayesian analysis to proof‐of‐concept trial planning and decision making

Decision making is a critical component of a new drug development process. Based on results from an early clinical trial such as a proof of concept trial, the sponsor can decide whether to continue, stop, or defer the development of the drug. To simplify and harmonize the decision‐making process, decision criteria have been proposed in the literature. One of them is to exam the location of a confidence bar relative to the target value and lower reference value of the treatment effect. In this research, we modify an existing approach by moving some of the “stop” decision to “consider” decision so that the chance of directly terminating the development of a potentially valuable drug can be reduced. As Bayesian analysis has certain flexibilities and can borrow historical information through an inferential prior, we apply the Bayesian analysis to the trial planning and decision making. Via a design prior, we can also calculate the probabilities of various decision outcomes in relationship with the sample size and the other parameters to help the study design. An example and a series of computations are used to illustrate the applications, assess the operating characteristics, and compare the performances of different approaches.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Predictors of Relapse in Problem Gambling: A Prospective Cohort Study

To explore the variation of predictors of relapse in treatment and support seeking gamblers. A prospective cohort study with 158 treatment and support seeking problem gamblers in South Australia. Key measures were selected using a consensus process with international experts in problem gambling and related addictions. The outcome measures were Victorian Gambling Screen (VGS) and behaviours related to gambling. Potential predictors were gambling related cognitions and urge, emotional disturbance, social support, sensation seeking traits, and levels of work and social functioning. Mean age of participants was 44 years (SD = 12.92 years) and 85 (54 %) were male. Median time for participants enrolment in the study was 8.38 months (IQR = 2.57 months). Patterns of completed measures for points in time included 116 (73.4 %) with at least a 3 month follow-up. Using generalised mixed-effects regression models we found gambling related urge was significantly associated with relapse in problem gambling as measured by VGS (OR 1.29; 95 % CI 1.12–1.49) and gambling behaviours (OR 1.16; 95 % CI 1.06–1.27). Gambling related cognitions were also significantly associated with VGS (OR 1.06; 95 % CI 1.01–1.12). There is consistent association between urge to gamble and relapse in problem gambling but estimates for other potential predictors may have been attenuated because of methodological limitations. This study also highlighted the challenges presented from a cohort study of treatment and support seeking problem gamblers.     

The implementation and the cultural adjustment of functional family therapy in a Dutch psychiatric day-treatment center

Because of the increasing severity of adolescent problem behavior, evidence-based practices are becoming of interest as an alternative to traditional treatment with the behavior problems of adolescents in juvenile justice settings. Despite interest in evidence-based practices, questions exist regarding whether or not evidence-based intervention models can be successfully transported to cultures other than those in which they were developed. This article describes the transportation process of an American evidence-based family therapy (Functional Family Therapy [FFT]) into the service delivery system of a psychiatric day treatment center for juvenile delinquents in Amsterdam. The characteristics of FFT that make it cross-culturally sensitive are discussed. Results from the changes in service delivery suggest FFT can be successfully implemented in international settings with adjustments to make the model fit the culture(s) of The Netherlands without changing the model of FFT itself.