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.     

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.     

“I Want to Feel Like a Full Man”: Conceptualizing Gay,Bisexual, and Heterosexual Men’s Sexual Difficulties

Current understandings of sexual difficulties originate from a model that is based on the study of heterosexual men and women. Most research has focused on sexual difficulties experienced by heterosexual men incapable of engaging in vaginal penetration. To better understand men’s perceptions and experiences of sexual difficulties, seven focus groups and 29 individual interviews were conducted with gay (n = 22), bisexual (n = 5), and heterosexual (n = 25) men. In addition, the extent to which difficulties reported by gay and bisexual men differ from heterosexual men was explored. Data were analyzed using thematic analysis applying an inductive approach. Two intercorrelated conceptualizations were identified: penis function (themes: medicalization, masculine identity, psychological consequences, coping mechanisms) and pain (themes: penile pain, pain during receptive anal sex). For the most part, gay, bisexual, and heterosexual men reported similar sexual difficulties; differences were evident regarding alternative masculinity, penis size competition, and pain during receptive anal sex. The results of this study demonstrate the complexity of men’s sexual difficulties and the important role of sociocultural, interpersonal, and psychological factors. Limitations and suggested directions for future research are outlined.     

Comics and visual biography: sequential art in social research

This article describes the use of comics in a mixed methods biographical research project aimed at understanding the experiences of vegans (i.e. people who eschew animal products such as meat, dairy and eggs). It begins with a discussion of Comics Studies as a growing interdisciplinary field of academic inquiry, and attempts to trace a connection between this and Visual Sociology more broadly. It then provides examples of the way in which comics were used in the project and the rationale underpinning this. Participants were asked to create comics about their lives, which aimed to supplement biographical interviews that had already taken place, eliciting rich ‘visual biographical’ data that an interview would not produce. Comics were also used as a mode of representation, whereby a ‘visual autoethnography’ was produced, outlining the author’s reflexive autobiographical relationship with veganism, and telling the story of the research. This project presented challenges, specifically around participation, ethics and anonymity, and data analysis. Despite this, the comics produced provided an unusual and valuable insight into the lives and experiences of vegans. To conclude, this article argues that the visual biographical data yielded through the use of the Comics medium represents a valuable tool in visual sociology.