Data envelopment analysis in the presence of measurement error: case study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data envelopment analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency [B. Hollingsworth, The measurement of efficiency and productivity of health care delivery. Health Economics 17(10) (2008), pp. 1107–1128], but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized [B.J. Gajewski, R. Lee, M. Bott, U. Piamjariyakul, and R.L. Taunton, On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process. Journal of Applied Statistics 36(9) (2009), pp. 933–944; J. Ruggiero, Data envelopment analysis with stochastic data. Journal of the Operational Research Society 55 (2004), pp. 1008–1012]. We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators^® to estimate nursing units’ efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.     

A new stratified three-stage unrelated randomized response model for estimating a rare sensitive attribute based on the Poisson distribution

This article suggests an efficient method of estimating a rare sensitive attribute which is assumed following Poisson distribution by using three-stage unrelated randomized response model instead of the Land et al. model (2011 Land, M., S. Singh, and S. A. Sedory. 2011. Estimation of a rare sensitive attribute using poisson distribution. Statistics 46 (3):351–60. doi:10.1080/02331888.2010.524300.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) when the population consists of some different sized clusters and clusters selected by probability proportional to size(:pps) sampling. A rare sensitive parameter is estimated by using pps sampling and equal probability two-stage sampling when the parameter of a rare unrelated attribute is assumed to be known and unknown.

We extend this method to the case of stratified population by applying stratified pps sampling and stratified equal probability two-stage sampling. An empirical study is carried out to show the efficiency of the two proposed methods when the parameter of a rare unrelated attribute is assumed to be known and unknown.     

Fitting competing risks data to bivariate Pareto models

This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model introduced by Sankaran and Nair (1993 Sankaran, P. G., and N. U. Nair. 1993. A bivariate Pareto model and its applications to reliability. Naval Research Logistics 40 (7):1013–1020. doi:10.1002/1520-6750(199312)40:7%3c1013::AID-NAV3220400711%3e3.0.CO;2-7.[Crossref], [Web of Science ®] , [Google Scholar]). We discuss the identifiability issues of these models and develop the maximum likelihood estimation procedures including their computational algorithms and model-diagnostic procedures. Simulations are conducted to examine the performance of the maximum likelihood estimation. Real data are analyzed for illustration.     

Identifying Changing Aviation Threat Environments Within an Adaptive Homeland Security Advisory System

A critical component of aviation security consists of screening passengers and baggage to protect airports and aircraft from terrorist threats. Advancements in screening device technology have increased the ability to detect these threats; however, specifying the operational configurations of these devices in response to changes in the threat environment can become difficult. This article proposes to use Fisher information as a statistical measure for detecting changes in the threat environment. The perceived risk of passengers, according to prescreening information and behavior analysis, is analyzed as the passengers sequentially enter the security checkpoint. The alarm responses from the devices used to detect threats are also analyzed to monitor significant changes in the frequency of threat items uncovered. The key results are that this information‐based measure can be used within the Homeland Security Advisory System to indicate changes in threat conditions in real time, and provide the flexibility of security screening detection devices to responsively and automatically adapt operational configurations to these changing threat conditions.     

How do I get my way? A meta-analytic review of research on influence tactics

Despite considerable research investigating the role of influence tactics on work-related outcomes in organizations, consensus on the effectiveness of influence tactics has been elusive. Specifically, there is a lack of integration concerning the relationships between proactive influence tactics and their outcomes. We investigate the effectiveness of 11 influence tactics from a comprehensive perspective using meta-analytic techniques. In particular, the current study focuses on relationships between each of the 11 influence tactics (i.e., rational persuasion, exchange, inspirational appeal, legitimating, apprising, pressure, collaboration, ingratiation, consultation, personal appeals, and coalition) and task- and relations-oriented outcomes. In addition, we investigate the moderating effects of the direction of influence tactics, measurement of influence tactics, singular influence tactic (versus use of a combination of influence tactics), independence of data sources, and study setting involved in the study. Regardless of task- and relations-oriented outcomes, based on 49 independent samples (N = 8987), our results show positive relationships between outcomes and rational persuasion, inspirational appeal, apprising, collaboration, ingratiation, consultation, and a negative relationship between pressure and outcomes. Rational persuasion is the only tactic which held stable positive relationships with both categories of outcomes regardless of moderating factors. Implications and directions for future research in the area of influence tactics are discussed.     

Never easy to say “sorry”: Exploring the interplay of crisis involvement,brand image,and message appeal in developing effective corporate apologies

This study examines how the interplay among crisis involvement, brand image, and message appeal impacts the effectiveness of an apology in the context of a corporate crisis. To determine apology effectiveness, a 2 (crisis involvement: high vs. low) × 2 (brand image: symbolic vs. functional) × 2 (message appeal type: emotional vs. informational) experimental design was employed. The results demonstrate that the three-way interaction is significant, especially in the low-crisis involvement condition. The study concludes with a discussion of theoretical and practical implications and directions for future research.     

The elites’ collective subjectivities of the history of ethnic politics in Malaysia

ABSTRACT

Malaysia is one of the multi-ethnic, multi-cultural and multi-religious countries in Southeast Asia. Due to the pluralistic nature of Malaysia, it has a political structure based on ethnic politics. The ethnic preferential policies affected most domains of this country. The objective of this article is to examine the origin and background of ethnic politics in Malaysia. Findings of this study indicate that, ethnic politics originated during the British colonial period, it became a tool used by the Barisan Nasional for the legitimacy of regime. Moreover, ethnic politics in Malaysia today is intertwined with religion. Besides, there is the dilemma of the choice between the interest of certain ethnic group and national interests. However, with the opposition coalition Pakatan Harapan won the election in 9th May, UMNO-led BN lost power and interrupted its 61 years control, which leaves us an interesting topic to think about the future of Malaysian ethnic politics.     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

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.