Cumulative cohort design for dose-finding

We introduce a new design for dose-finding in the context of toxicity studies for which it is assumed that toxicity increases with dose. The goal is to identify the maximum tolerated dose, which is taken to be the dose associated with a prespecified “target” toxicity rate. The decision to decrease, increase or repeat a dose for the next subject depends on how far an estimated toxicity rate at the current dose is from the target. The size of the window within which the current dose will be repeated is obtained based on the theory of Markov chains as applied to group up-and-down designs. But whereas the treatment allocation rule in Markovian group up-and-down designs is only based on information from the current cohort of subjects, the treatment allocation rule for the proposed design is based on the cumulative information at the current dose. We then consider an extension of this new design for clinical trials in which the subject's outcome is not known immediately. The new design is compared to the continual reassessment method.     

From undesirable immigrant to model minority: The success story of Chinese in New Zealand

Attitudes of the wider society towards Chinese in New Zealand have undergone a substantial change within the past 30 years, from the negative ‘undesirable immigrant’ to a highly positive ‘model minority’. Unlike many other ethnic minorities in New Zealand, Chinese are seen now to be an extremely acceptable group. This article examines some of the historical and social events that have culminated in this dramatic change in attitude.     

Primary Care Screening of Depression and Treatment Engagement in a University Health Center: A Retrospective Analysis

Abstract

Objectives: This retrospective study analyzed a primary care depression screening initiative in a large urban university health center. Depression detection, treatment status, and engagement data are presented. Participants: Participants were 3,713 graduate and undergraduate students who presented consecutively for primary care services between January and April 2006. Methods: A standardized 2-tiered screening approach for an inception cohort of students utilizing primary services. Primary care providers were trained to triage students with depressive symptoms. Results: Six percent of participants had clinically significant depressive symptoms (CSD). Severe depressive symptoms were found in less than 1.0% of participants. Male rates of severe depressive symptoms were more than double that of females. Only 35.7% of untreated depressed participants started treatment within 30 days following identification. Conclusions: Systematic primary care depression screening in a college health center is a promising approach to identify untreated students with depression. More study is needed to improve rates of treatment engagement.     

Exploring alternative pathways out of poverty: making connections between domestic violence and employment practices

In this article we report on the research findings from our exploratory study examining connections between Australian responses to two important social issues: domestic violence and employment. Although international literature particularly from North America, comprehensively addresses the connections between domestic violence and employment, there is a paucity of Australian research on this topic. Intrigued by this as both practitioners and researchers, we sought to discover whether the problems, solutions and conclusions addressed internationally were relevant to an Australian context. The result was a unique contribution to Australian knowledge and practice where we demonstrate the applicability of some aspects of North American approaches to Australia and document innovative local practices in the field. In this article we argue for a more comprehensive approach in responses to the connections between domestic violence and employment as an alternative pathway out of poverty for women affected by domestic violence.     

Online Donation Experiences,Donation Awareness,and Intention of Future Donation Among Teenagers in South Korea

Younger generations have become less likely to donate than older generations. Online fundraising might be a solution to encourage young donors. This study examines the mediating effects of online donation on the donation awareness of teenagers, as well as the pathway from online donation to an intention to donate in the future. Using data collected from 287 teenagers through an online survey, the study found that online donation experience has positive effects on the frequency of future donation, mediated by donation awareness. These results suggest that nonprofit organizations should consider online donation platforms to be important fundraising mechanisms in encouraging young donors. Future research is need in order to develop the theoretical models of online donation as well as investigate the applicability of the models, especially for young generations.     

On the test of liu and chow's procedure for assessing equivalence in variability of bioavailability

Similar to Schuirmann's two one-sided tests procedure for assessment of bioequivalence in average bioavailability (Schuirmann,), Liu and Chow proposed a two one-sided tests procedure for assessment of equivalence of variability of bioavailability. Their procedure is derived based on the correlation between crossover differences and subject totals. In this paper, we examined the performance of their test procedure in terms of its test size and power for various situations where the intersubject variability and the intrasubject variability of the test drug product are relatively larger, similar, and smaller than that of the intrasubject variability of the reference drug product.     

The Impact of Complementary Agglomeration and Multi‐unit Systems on New Product Introduction

This study explores the relationship between organizational agglomeration and new product introduction. It proposes that product‐complementary agglomeration increases the likelihood of new product introduction, but that the effect on new product introduction is non‐linear. In addition, the influence of agglomeration on new product introduction is contingent on organizational form (i.e. multi‐unit form or independent form). Using longitudinal data for the hospital industry in Taiwan from 1997 to 2002, we found that the relationship between product‐complementary agglomeration and new product introduction is an upward trending hooked curve. As the degree of complementary agglomeration increases, the likelihood of introducing new products also increases, but the rate of increase diminishes with the degree of complementary agglomeration. In addition, we also found that the positive effect of product‐complementary agglomeration on new product introduction is stronger for independent firms than for multi‐unit firms.     

Measuring robustness for weighted distributions: Bayesian perspective

There are many situations where the usual random sample from a population of interest is not available, due to the data having unequal probabilities of entering the sample. The method of weighted distributions models this ascertainment bias by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of the events as observed and recorded. We consider two different classes of contaminated or mixture of weight functions, Γ _a ={w(x):w(x)=(1−ε)w ₀(x)+εq(x),q∈Q} and Γ _g ={w(x):w(x)=w ₀ ^1−ε (x)q ^ε(x),q∈Q} wherew ₀(x) is the elicited weighted function,Q is a class of positive functions and 0≤ε≤1 is a small number. Also, we study the local variation of ϕ-divergence over classes Γ _a and Γ _g . We devote on measuring robustness using divergence measures which is based on the Bayesian approach. Two examples will be studied.     

Random Level-Shift Time Series Models,ARIMA Approximations,and Level-Shift Detection

The main purpose of this article is to assess the performance of autoregressive integrated moving average (ARIMA) models when occasional level shifts occur in the time series under study. A random level-shift time series model that allows the level of the process to change occasionally is introduced. Between two consecutive changes, the process behaves like the usual autoregressive moving average (ARMA) process. In practice, a series generated from a random level-shift ARMA (RLARMA) model may be misspecified as an ARIMA process. The efficiency of this ARIMA approximation with respect to estimation of current level and forecasting is investigated. The results of examining a special case of an RLARMA model indicate that the ARIMA approximations are inadequate for estimating the current level, but they are robust for forecasting future observations except when there is a very low frequency of level shifts or when the series are highly negatively correlated. A level-shift detection procedure is presented to handle the low-frequency level-shift phenomena, and its usefulness in building models for forecasting is demonstrated.