Bayesian hierarchical models for adaptive basket trial designs

Basket trials evaluate a single drug targeting a single genetic variant in multiple cancer cohorts. Empirical findings suggest that treatment efficacy across baskets may be heterogeneous. Most modern basket trial designs use Bayesian methods. These methods require the prior specification of at least one parameter that permits information sharing across baskets. In this study, we provide recommendations for selecting a prior for scale parameters for adaptive basket trials by using Bayesian hierarchical modeling. Heterogeneity among baskets attracts much attention in basket trial research, and substantial heterogeneity challenges the basic assumption of exchangeability of Bayesian hierarchical approach. Thus, we also allowed each stratum-specific parameter to be exchangeable or nonexchangeable with similar strata by using data observed in an interim analysis. Through a simulation study, we evaluated the overall performance of our design based on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs.     

Nonlinear measures of association with kernel canonical correlation analysis and applications

Measures of association between two sets of random variables have long been of interest to statisticians. The classical canonical correlation analysis (LCCA) can characterize, but also is limited to, linear association. This article introduces a nonlinear and nonparametric kernel method for association study and proposes a new independence test for two sets of variables. This nonlinear kernel canonical correlation analysis (KCCA) can also be applied to the nonlinear discriminant analysis. Implementation issues are discussed. We place the implementation of KCCA in the framework of classical LCCA via a sequence of independent systems in the kernel associated Hilbert spaces. Such a placement provides an easy way to carry out the KCCA. Numerical experiments and comparison with other nonparametric methods are presented.     

Use of a two-sided tolerance interval in the design and evaluation of biosimilarity in clinical studies

In assessing biosimilarity between two products, the question to ask is always “How similar is similar?” Traditionally, the equivalence of the means between products is the primary consideration in a clinical trial. This study suggests an alternative assessment for testing a certain percentage of the population of differences lying within a prespecified interval. In doing so, the accuracy and precision are assessed simultaneously by judging whether a two-sided tolerance interval falls within a prespecified acceptance range. We further derive an asymptotic distribution of the tolerance limits to determine the sample size for achieving a targeted level of power. Our numerical study shows that the proposed two-sided tolerance interval test controls the type I error rate and provides sufficient power. A real example is presented to illustrate our proposed approach.     

Statistical frameworks for setting cutoff points of metabolic syndrome criteria

We propose three statistical frameworks for determining the cutoff points of metabolic syndrome (MetS) criteria, consisting of six components that are the same as in widely used MetS definitions, e.g., the 2004 updated NCEP-ATPIII criteria. Several international organizations have proposed MetS definitions; no literature indicates that any of these definitions is based on statistical frameworks. For all the three frameworks, the cutoff points are set to maximize the observed prevalence rate of stroke and DM. The three frameworks differ in assumptions on the joint distribution of the six components. Using the cohort data from a regional hospital in Taiwan, we illustrate applications of the three frameworks and compare them with the updated NCEP-ATPIII definition and the 2009 consensus definition of IDF and AHA/NHLBI. The performance measure is the odds ratio, the odds of getting stroke or DM within subjects with MetS divided by the analogous odds for subjects without MetS. Our numerical results show that the odds ratios of the three frameworks are higher than those of the updated-NCEP and consensus definitions, showing that the proposed frameworks seem to provide a better association of MetS with stroke and DM.     

Human resource rightsizing using centralized data envelopment analysis: Evidence from Taiwan's Airports

Centralized human resource reallocation involves deciding how to fit change in a way that maximizes aggregated outputs. This paper presents an alternative approach to reallocating human resources. Specifically, an aggregated concept in the form of data envelopment analysis (DEA) is used to construct a modified centralized DEA model that is combined with a Russell measure and applied to three different human resource reallocation policies. An application to illustrate manpower rightsizing in an airport organizational setting in Taiwan is also demonstrated using centralized DEA for regular and/or contracted employees. The results demonstrate that the application is effective in reducing and/or transferring staff, while maximizing organizational output levels and implementing smooth downsizing. The results also present directions for possible improvements in workplace efficiency.     

A Virtual Field Practicum: Building Core Competencies Prior to Agency Placement

ABSTRACT

Providing effective services to clients with increasingly severe challenges in an era of fiscal constraints calls for schools of social work to assume greater responsibility for preparing interns for clinical practice. This article describes a virtual field practicum (VFP), an online skill-building experience designed to meet this need and reports preliminary findings. The VFP employs client simulation and other experiential activities; its intent is to foster student acquisition of core competencies prior to agency placement. Longitudinal data were collected on VFP students and their traditional counterparts across 4 semesters. Results show that VFP students performed as well, if not slightly better, on competency measures than traditional students, suggesting that this model is a viable option for educators to consider.     

Support in the Workplace: Buffering the Deleterious Effects of Work–Family Conflict on Child Welfare Workers’ Well-Being and Job Burnout

ABSTRACT

We examined the impact of work–family conflict (WFC) on worker well-being and job burnout, as well as the role of social support in the workplace as a potential buffer against the influence of WFC. Results of hierarchical multiple regression analyses in a cross-sectional sample of 361 public child welfare workers indicate that WFC was related to reduced well-being and increased job burnout, whereas supervisory support moderated the relationship between WFC and well-being. Findings from this study hold implications to workforce management practices aimed at improving worker well-being in human service and child welfare organizations. Human resource management policies designed to manage worker schedules should aim to facilitate workers’ ability to meet work and family demands through programs such as flexible work schedules and telecommuting. Recommendations for future research on WFC and worker well-being are discussed.     

Establishing consistency across all regions in a multi-regional clinical trial

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multi-regional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in a global trial, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each region. However, most of the recent approaches developed for the design and evaluation of MRCTs focus on establishing criteria to examine whether the overall results from the MRCT can be applied to a specific region. In this paper, we use the consistency criterion of Method 1 from the Japanese Ministry of Health, Labour and Welfare (MHLW) guidance to assess whether the overall results from the MRCT can be applied to all regions. Sample size determination for the MRCT is also provided to take all the consistency criteria from each individual region into account. Numerical examples are given to illustrate applications of the proposed approach.     

Benefits and limitations of panel data

Observations for a number of cross-sectional units over time have become increasingly available. The new data sources enable econometricians to construct and test more complicated behavioral models than a single cross sectional or time series data set would allow. The availability of new data sources, however, also raises new issues. In this paper we review some basic econo- metric methods that have been used to analyze such data sets. We also indicate areas of research where panel data may be useful.     

Asymptotic distribution of quasi-maximum likelihood estimation of dynamic panels using long difference transformation when both <Emphasis Type="Italic">N</Emphasis> and <Emphasis Type="Italic">T</Emphasis> are large

This note shows that the asymptotic properties of the quasi-maximum likelihood estimation for dynamic panel models can be easily derived by following the approach of Grassetti (Stat Methods Appl 20:221–240, 2011) to take the long difference to remove the time-invariant individual specific effects.