Bayesian averaging of classical estimates in asymmetric vector autoregressive models

The estimated vector autoregressive (VAR) model is sensitive to model misspecifications, resulting to biased and inconsistent parameter estimates. This article extends the Bayesian averaging of classical estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of asymmetric VAR models. The proposed procedure was applied to simulated data from various forms of model misspecifications. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and moving average (MA) terms were omitted, the proposed procedure gives robust results and better forecasts than the automatically selected equal lag-length VAR model.     

Negotiating Tensions between Methodology and Procedural Ethics

In this commentary, I consider what can go wrong in research when tensions arise between methodology and procedural ethics. I recount difficulties negotiating and implementing a participant recruitment strategy during my doctoral research project, which aimed to explore the experiences of people affected by dementia in the United Kingdom who were disengaged from services. To access this hard-to-reach population, I intended to adopt an informal recruitment strategy, snowball sampling from personal contacts and striking up conversations in public places. The procedural ethics committee were unhappy with this approach, deeming it potentially coercive. They suggested a more formal recruitment strategy enacted via emailing community organisations and churches. This approach entailed practical consequences that ultimately weakened the study sample, data and findings. This case raises questions about the negotiation of tensions between methodology and procedural ethics in gerontological research.     

Do flexible work schedules reduce turnover in U.S. federal agencies?

Agency theory suggests that when agencies adopt flexible work schedules, employees will be more likely to remain with the organization, because these programs demonstrate that the organization cares about their well-being in that flexible work schedules give them more flexibility regarding when, where, and how they perform their work. To test this proposition, cross-sectional panel data at the agency level were obtained from two federal government sources: Federal Employee Viewpoint Survey and FedScope. Furthermore, transfers and quits were the two forms of turnover examined. After lagging the independent variables behind turnover over several years so as to provide a robust test of causality, the results show only limited support for agency theory. Specifically, teleworking was found to lower quits. However, teleworking was not found to reduce transfers or turnover, in general. Furthermore, alternative work schedules were not found to have an impact on quits, transfers, or turnover generally.     

Making Professional Decisions in Research: Measurement and Key Predictors

The professional decision-making in research (PDR) measure was administered to 400 National Institutes of Health (NIH)-funded and industry-funded investigators, along with measures of cynicism, moral disengagement, compliance disengagement, impulsivity, work stressors, knowledge of responsible conduct of research (RCR), and socially desirable response tendencies. Negative associations were found for the PDR and measures of cynicism, moral disengagement, and compliance disengagement, while positive associations were found for the PDR and RCR knowledge and positive urgency, an impulsivity subscale. PDR scores were not related to socially desirable responding, or to measures of work stressors and the remaining impulsivity subscales. In a multivariate logistic regression analysis, lower moral disengagement scores, higher RCR knowledge, and identifying the United States as one’s nation of origin emerged as key predictors of stronger performance on the PDR. The implications of these findings for understanding the measurement of decision-making in research and future directions for research and RCR education are discussed.     

Effects of Network Approval on Accounts of Commitment Trajectories in Intraracial and Interracial Relationships

The current study used dyadic data to investigate the impact of relationship type and social support on the retrospective accounts of commitment trajectories of romantic relationships. Past research suggests that social support is a positive contributor to relationship stability and commitment, which may be especially true for partners in interracial relationships who face broader societal opposition than intraracial couples. Using multilevel modeling, we investigated the effects of sex, relationship type, and social support on reports of commitment. Results showed differences in trajectories of commitment based on couple type (interracial vs. intraracial) for both men and women. Social support was found to have an especially strong impact for women in interracial relationships compared with women in intraracial relationships, but there was no differential impact among men.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

Coercive controlling behaviors in intimate partner violence in male same-sex relationships:A mixed-methods study

Using mixed methods, we explored the role of coercive controlling behaviors in a high-risk sample of 126 men in violent same-sex relationships. Contrary to a prediction that separate factors of physical violence and coercive control might emerge, a simple principle components analysis supported that male same-sex relationship intimate partner violence (IPV) is essentially unidimensional. Qualitative narratives supported a single latent factor solution of violence, and that coercive controlling behaviors better detect IPV dynamics within the same violent encounters (i.e., weapon use), even when compared to profiles defined by physical violence. Narratives also highlighted gender-different tactics of coercion used, underscoring importance of context-based assessments.