Sample size estimation for comparing rates of change in K-group repeated count outcomes

Sample size estimation for comparing the rates of change in two-arm repeated measurements has been investigated by many investigators. In contrast, the literature has paid relatively less attention to sample size estimation for studies with multi-arm repeated measurements where the design and data analysis can be more complex than two-arm trials. For continuous outcomes, Jung and Ahn (2004 Jung, S., Ahn, C. (2004). K-sample test and sample size calculation for comparing slopes in data with repeated measurements. Biometrical J. 46(5):554–564.[Crossref], [Web of Science ®] , [Google Scholar]) and Zhang and Ahn (2013 Zhang, S., Ahn, C. (2013). Sample size calculation for comparing time-averaged responses in k-group repeated measurement studies. Comput. Stat. Data Anal. 58:283–291.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) have presented sample size formulas to compare the rates of change and time-averaged responses in multi-arm trials, using the generalized estimating equation (GEE) approach. To our knowledge, there has been no corresponding development for multi-arm trials with count outcomes. We present a sample size formula for comparing the rates of change in multi-arm repeated count outcomes using the GEE approach that accommodates various correlation structures, missing data patterns, and unbalanced designs. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of designing configurations. Simulation results suggest that empirical type I error and power are maintained close to their nominal levels. The proposed method is illustrated using an epileptic clinical trial example.     

Arenas of comfort and conflict: peer relationship events and young people's educational attainment

Previous research suggests that developments in young people's peer relationships may either compound or alleviate the adverse impacts of other major life changes during adolescence. We explored this proposition with respect to young people's educational attainment upon leaving high school, using longitudinal data from a large cohort of Australian secondary school students (n?=?1612) who have taken part in the Our Lives research study between the ages of 12/13 and 19/20 years. Our analysis focused on the role of peer relationship events such bullying, friendship problems, falling in love, and breaking up with someone. Bullying and romantic involvement were associated with lower odds of receiving a competitive tertiary entrance rank at the end of high school. However, close, resilient friendships – in which status and identity conflicts may be more easily tolerated and resolved – may help to offset the role of these other events. As well as reviewing the consequences of our findings for young people's educational and occupational trajectories in the longer-term, we highlight their implications for future research and policy in this area.     

Life Course Status and Exchanges of Support Between Young Adults and Parents

The authors investigated intergenerational support exchanges in relation to young adults' life course status. In a sample of 2,022 young adults (ages 18–34 years) in The Netherlands, single young adults reported receiving more advice from parents than married young adults, and those with children of their own received more practical support. Married young adults and young adults with children provided less support to parents than, respectively, single young adults and young adults without children. Congruent with the life course perspective, the authors' findings suggest that the intergenerational support network is both durable and flexible, responding to the resources and needs associated with an individual's life course status.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Risk of Extreme Events in Multiobjective Decision Trees Part 1. Severe Events

Earlier work with decision trees identified nonseparability as an obstacle to minimizing the conditional expected value, a measure of the risk of extreme events, by the well-known method of averaging out and folding back. This first of two companion papers addresses the conditional expected value that is defined as the expected outcome assuming the exceedance of a threshold β, where β is preselected by the decision maker. An approach is proposed to overcome the need to evaluate all policies in order to identify the optimal policy. The approach is based on the insight that the conditional expected value is separable into two constituent elements of risk and can thus be optimized along with other objectives, including the unconditional expected value of the outcome, by using a multiobjective decision tree. An example of sequential decision making for improving highway capacity is given.     

A Framework to Understand Extreme Space Weather Event Probability

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well‐being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst ) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.     

It's Complicated: The 2014–2015 U.S. Measles Outbreak and Parents’ Vaccination Beliefs,Confidence, and Intentions

While it seems intuitive that highly visible vaccine‐preventable disease outbreaks should impact perceptions of disease risk and facilitate vaccination, few empirical studies exist to confirm or dispel these beliefs. This study investigates the impact of the 2014–2015 Disneyland measles outbreak on parents’ vaccination attitudes and future vaccination intentions. The analysis relies on a pair of public opinion surveys of American parents with at least one child under the age of six (N = 1,000 across each survey). Controlling for basic demographics, we found higher levels of reported confidence in the safety and efficacy of childhood vaccinations in our follow‐up data collection. However, this confidence was also accompanied by elevated levels of concern toward childhood vaccines among American parents. We then examined how different subgroups in the population scored on these measures before and after the outbreak. We found that parents with high levels of interest in the topic of vaccines and a child who is not fully upto date with the recommended vaccination schedule reported more supportive attitudes toward vaccines. However, future intentions to follow the recommended vaccination schedule were not positively impacted by the outbreak. Possible explanations for these results and implications for vaccination outreach are discussed.     

对学习历史事件法的一种改进

依据组织学习理论,对学习历史事件法进行了改进。由于企业的经验知识不完全等同于隐性知识,学习历史事件法同样不能直接应用于隐性知识的转化,为此,提出了适合隐性知识转化的改进型学习历史事件法。改进充分考虑了隐性知识的特征,并专门对隐性知识和经验知识进行了区分。同时,改进的学习历史事件法充分考虑了企业应用的可操作性,保留了量表的形式。最后,将该方法应用于MIS项目开发中,对新改进的量表进行了定量解析。     

Behavioral pathways to private well risk mitigation: A structural equation modeling approach

Complex, multihazard risks such as private groundwater contamination necessitate multiannual risk reduction actions including seasonal, weather-based hazard evaluations. In the Republic of Ireland (ROI), high rural reliance on unregulated private wells renders behavior promotion a vital instrument toward safeguarding household health from waterborne infection. However, to date, pathways between behavioral predictors remain unknown while latent constructs such as extreme weather event (EWE) risk perception and self-efficacy (perceived behavioral competency) have yet to be sufficiently explored. Accordingly, a nationwide survey of 560 Irish private well owners was conducted, with structural equation modeling (SEM) employed to identify underlying relationships determining key supply management behaviors. The pathway analysis (SEM) approach was used to model three binary outcomes: information seeking, post-EWE action, and well testing behavior. Upon development of optimal models, perceived self-efficacy emerged as a significant direct and/or indirect driver of all three behavior types—demonstrating the greatest indirect effect (β = −0.057) on adoption of post-EWE actions and greatest direct (β = 0.222) and total effect (β = 0.245) on supply testing. Perceived self-efficacy inversely influenced EWE risk perception in all three models but positively influenced supply awareness (where present). Notably, the presence of a vulnerable (infant and/or elderly) household member negatively influenced adoption of post-EWE actions (β = −0.131, p = 0.016). Results suggest that residential and age-related factors constitute key demographic variables influencing risk mitigation and are strongly mediated by cognitive variables—particularly self-efficacy. Study findings may help contextualize predictors of private water supply management, providing a basis for future risk-based water interventions.     

Monitoring exponential data using two-sided control charts with runs rules

In this article, new two-sided control charts with runs rules, suitable for the monitoring of exponential data, are proposed and studied. The proposed schemes are suitable to identify changes (upward or downward) in the mean of an exponential distribution. Also, they have the desired in-control performance as well as unbiased performance. Guidelines for the most effective scheme in practice are provided, along with comparisons with other competitive schemes. Finally, the practical application of the proposed schemes is also discussed.