Longitudinal patterns of anger reactivity and risk‐taking: The role of peer‐context

The current study examined the interplay between children's dispositional anger and susceptibility to peers' influence in increasing children's risk‐taking behaviors. Participants in the current study were children from a larger study of temperament and social–emotional development who were followed across 9, 24, 36, 48, and 60 months. Dispositional anger was measured using mothers' reports across 9 and 48 months. At 60 months, children played a risk‐taking computer game in presence of an unfamiliar peer who watched the child play. The child's risk‐taking was assessed during the game as the unfamiliar peers' reactions were coded based on comments that were peer directed, reflective of praising the target child's performance, or object directed, indicative of excitement toward the game. A latent profile analysis revealed three longitudinal anger profiles across infancy to early childhood: high stable, average stable, and low stable anger. Results suggested that as peers' object‐directed comments predicted risk‐taking independent of children's anger, the association between peer‐directed comments and risk‐taking was dependent on children's dispositional anger. Specifically, when peers praised the target child's performance, children in the high stable anger profile showed increased risk‐taking propensity. Findings are discussed based on the importance of considering both temperamental characteristics and aspects of the peer context in relation to children's risk‐taking.     

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient‐level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.     

Individual mobility in a stationary population

An increasing population facilitates individual mobility. One of the consequences of moving towards the inevitable stationary population is that mobility will become more difficult.     

Following the Rules? Candidate Strategy in Presidential Primaries*

Objectives. The competitiveness of the 2008 presidential primaries in both the Republican and Democratic parties has prompted a reconsideration of the role of delegate‐selection rules in influencing the strategic behavior of presidential candidates. Using advertising and candidate state‐visit data from the 2004 and 2008 presidential nominating campaigns, we reexamine the strategies presidential candidates use when competing for the nomination of their party. Methods. Using the Wisconsin Advertising Project Data from 2004 and 2008, we estimate several multiple regressions designed to analyze the factors predicting candidate visits and advertising. Results. We find that, to a large extent, the rules of the game help predict where candidates allocate their political advertising and campaign stops; candidates consider whether a contest is a primary or caucus, they pay attention to how many delegates are at stake, and they consider whether a state's delegate‐allocation method is largely proportional or winner take all. Yet we also find some differences in how the rules influence frontrunners and long‐shot candidates, and we discover how other factors, including a candidate's access to financial resources, influence the allocation of ads and visits. Conclusion. Our findings offer some of the first empirical evidence for the idea that a state's delegate‐allocation method influences candidates' resource‐allocation behavior. That these rules matter at all is somewhat of a surprise given that the delegate‐allocation methods used by states have become more homogenous within each party.     

The Impacts of Alliance Purpose and Partner Similarity on Alliance Governance

It is generally recognized that the governance structure of an alliance has an impact on its probability of success. In this study, we examine the choice between two alternative alliance governance structures: equity and non-equity. Drawing from transaction cost economics, two sets of factors, namely alliance purpose (R&D or marketing) and cultural distance between partners, are hypothesized to influence the above choice. We further hypothesize that collaborative R&D alliances, where both parties contribute technical knowledge, are more likely to lead to the formation of equity alliances than non-collaborative research agreements, where only one partner may be doing the research work. Based on a sample of 2407 alliances formed in the global biotechnology industry, we find partial support for the hypothesized relationships. Specifically, we find that collaborative R&D alliances are more likely to be equity alliances, whereas non-collaborative R&D alliances do not have any impact on the choice of the governance form. We also find that alliances formed with a marketing purpose are less likely to be equity alliances. We did not find any relationship between cultural distance and the choice of equity alliances.     

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.