Analysis of dose-response in flexible dose titration clinical studies

Assessing dose-response from flexible-dose clinical trials (e.g., titration or dose escalation studies) is challenging and often problematic due to the selection bias caused by 'titration-to-response'. We investigate the performance of a dynamic linear mixed-effects (DLME) model and marginal structural model (MSM) in evaluating dose-response from flexible-dose titration clinical trials via simulations. The simulation results demonstrated that DLME models with previous exposure as a time-varying covariate may provide an unbiased and efficient estimator to recover exposure-response relationship from flexible-dose clinical trials. Although the MSM models with independent and exchangeable working correlations appeared to be able to recover the right direction of the dose-response relationship, it tended to over-correct selection bias and overestimated the underlying true dose-response. The MSM estimators were also associated with large variability in the parameter estimates. Therefore, DLME may be an appropriate modeling option in identifying dose-response when data from fixed-dose studies are absent or a fixed-dose design is unethical to be implemented.     

Implications of random cut‐points theory for the Mann‐Whitney and binomial tests

Through random cut‐points theory, the author extends inference for ordered categorical data to the unspecified continuum underlying the ordered categories. He shows that a random cut‐point Mann‐Whitney test yields slightly smaller p‐values than the conventional test for most data. However, when at least P% of the data lie in one of the k categories (with P = 80 for k = 2, P = 67 for k = 3,…, P = 18 for k = 30), he also shows that the conventional test can yield much smaller p‐values, and hence misleadingly liberal inference for the underlying continuum. The author derives formulas for exact tests; for k = 2, the Mann‐Whitney test is but a binomial test.     

Strengthening the Domestic and Family Violence Workforce: Key Questions

ABSTRACT

The significant impact of domestic and family violence in Australia calls for a workforce that is both highly skilled and capable of meeting the demands of complex and challenging jobs. Yet despite the increasingly national visibility of domestic violence, the workers—and work itself—have largely remained invisible. We argue for a shift in conversation, highlighting the need for a workforce approach that is not only strategic but that also applies a theorised lens to domestic and family violence work inclusive of both gender and Indigeneity.

IMPLICATIONS

• The domestic and family violence workforce and workers should be made more visible to better support development in this sector.

• A workforce development strategy is needed to build knowledge about who is doing this work, the nature of the work, workplace structures and cultures, and work environments and conditions.

• The significance of gendered power relations, Indigeneity, high risk, trauma environments, and emotional labour in domestic and family violence work cannot be ignored in development strategies.

     

ON THE POWER BREAKDOWN OF THE NEGATIVE EXPONENTIAL DISPARITY TESTS

The generalized negative exponential disparity, discussed in Bhandari et al. (Robust inference in parametric models using the family of generalized negative exponential disparities, 2006, ANZJS, 48 , 95–114), represents an important class of disparity measures that generates efficient estimators and tests with strong robustness properties. In their paper, however, Bhandari et al. failed to provide a sharp lower bound for the power breakdown point of the corresponding tests. This was acknowledged by the authors, who indicated the possible existence of a sharper bound, but noted that they did not “have a proof at this point”. In this paper we provide an improved bound for this power breakdown point, and show with an example how this can enhance the existing results.     

Transitivity and fuzzy preferences

The authors would like to thank a referee at Social Choice and Welfare for insightful comments.     

Background and Adjustment Capacity of Sexually Abused Girls and their Perceptions of Intervention

The main objective of the study was to understand the adjustment capacity of sexually abused girls in the age group 13–18 years in Kolkata, India. The study also attempted to understand how the sexually abused girls perceived the psychological interventions, i.e. individual and group counselling, which they had received. A group of 120 sexually abused Indian girls and 120 non‐sexually abused Indian schoolgirls residing in Kolkata Metropolitan City and its suburbs were studied. Findings revealed that the majority of the sexually abused girls came from nuclear and poor families (93.3%) with a low educational background. More than two‐thirds (73.3%) were lured with promises of job prospects, marriage and a better life and then abused sexually. Emotional and social adjustment capacity of sexually abused and non‐sexually abused girls differed significantly (p < 0.05). Overall, the perception of more than two‐thirds of the sexually abused girls of the rehabilitation homes they lived in was found to be positive. Further, the girls who reported they gained considerably from counselling had a better adjustment capacity compared to those who reported they did not benefit from counselling. Copyright © 2011 John Wiley & Sons, Ltd.     

Contractor Accreditation: A Probabilistic Model

The selection of competent contractors is a critical function in all business organizations. In contrast to other types of vendors (e.g., distributors, manufacturers, etc.), contractors are typically accredited before any business transaction takes place. In such situations, there is often a considerable amount of uncertainty associated with the accreditation process. This research presents a probabilistic model for accrediting contractors. We discuss a methodology in which probability measures are used to capture the uncertainty inherent in the decision process. These probabilities are estimated from data on (i) past applicants and (ii) their eventual performance, if accredited. Furthermore, these probabilities are used to determine when additional information about an applicant should be collected, as well as what kind of information would be most relevant for the vendor under consideration.