Designing and analyzing clinical trials for personalized medicine via Bayesian models

Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.     

Natural disaster and risk-sharing behavior: Evidence from rural Bangladesh

Journal of Risk and Uncertainty - Using a unique field experiment in rural Bangladesh, this paper investigates how exposure to a natural disaster affects risk-sharing behavior. We conducted a...     

Social psychology,consumer culture and neoliberalism: A response to Phelps and White (2018)

The following essay responds to three main issues raised by Phelps and White (2018) in their critical commentary on our article (McDonald, Gough, Wearing & Deville, 2017). The first concerns the lack of precision in the conceptualisation of neoliberalism and the recent threats to it as we enter a potentially new phase of capitalism. While we share Phelps and White's concern, we argue that there is value in continuing to use neoliberalism as a concept for understanding some aspects of social behaviour. As to recent threats to neoliberalism, evidence indicates that it will continue to persist in the immediate future. To deal with neoliberalism's conceptual problems in social psychology, Phelps and White advance the potential theory of a ‘market‐derived logics’. We commend the authors for pursuing this endeavor, however, we caution that care needs to be taken in its conceptualisation. Lastly, we discuss Phelps and White's disciplinary reflections on social psychology.     

SALIENCE AND THE GOVERNMENT PROVISION OF PUBLIC GOODS

This paper examines how behavioral biases caused by salient events affect the government provision of public goods. We develop a theory in which competing communities lobby the government for allocations of a local public good. Salient events bias community demands for the good, which results in inefficient allocations. We empirically test this theory using salient wildfires and government projects to reduce wildfire risk. Wildfires reduce risk to nearby communities, but may increase demand for fuels management projects because of biases induced by salient wildfires. We find that communities experiencing recent nearby fires are more likely to receive fuels management projects. (JEL D03, H41, Q24)     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Fishing Households,Social Support,and Depression after the Deepwater Horizon Oil Spill

This study contributes to the growing body of knowledge on mental health in the aftermath of disasters by examining depressive symptoms among residents of the U.S. Gulf Coast region 6 years after the onset of the Deepwater Horizon oil spill (DHOS). Using data from the Survey of Trauma, Resilience, and Opportunity in Neighborhoods in the Gulf (STRONG), we test how social support and ties to the fishing industry are related to the likelihood of a positive depression screen. The results show that, among most residents of the Gulf Coast region, social support holds an inverse relationship with the likelihood of a positive depression screen. However, among fishing households, greater social support is associated with a higher probability of screening positive for depression. By showing that fishing households with greater social support are more susceptible to depressive symptoms in this setting, our results uncover a potentially important mechanism that contributes to the unique vulnerability of fishers, which in turn holds implications for differential impacts across social groups in environmental disaster contexts.     

A Population-Based Study of Coupling and Physical Activity by Sexual Orientation for Men

ABSTRACT

Research has suggested that men in relationships are more physically active than men who are single. This study provides a weighted analysis of physical activity by coupling status for men of different sexual orientations. Aggregated data from the United States 2013–2014 National Health Interview Survey were used to conduct multivariate logistic regression analyses. Compared to straight men (n = 29,926), gay men (n = 623) were less likely to be in a relationship (AOR 0.32, CI: 0.25–0.41). Coupled gay men did more physical activity than coupled straight men and were 1.62 (CI: 1.05–2.50) times more likely to be active, 1.67 (CI: 1.10–2.51) times more likely to be high active, 1.89 (CI: 1.24–2.89) times more likely to engage in muscle-strengthening activities, and 2.00 (CI: 1.28–3.11) times more likely to meet aerobic and muscle-strengthening recommendations. Coupling facilitates physical activity for men. However, more research is needed to help explore underlying mechanisms for differences by sexuality.     

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty

Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.