Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

The determinants of transnational entrepreneurship and transnational ties’ dynamics among immigrant entrepreneurs in ICT sector in Italy

This article contributes to the rapidly growing literature on transnational immigrant entrepreneurship by analyzing the determinants of transnational entrepreneurial engagement among immigrants in the ICT sector in Italy. We investigate which factors influence the rise or decline of transnational entrepreneurial involvement with a home country. Our results indicate that longer residence in Italy is associated with smaller propensity to become a transnational entrepreneur. Moreover, we find that the type of transnational ties and the network size have a substantial impact on the dynamics of transnational entrepreneurial engagement.     

Talk-Aloud Protocols during Conditional Discrimination Training and Equivalence Class Formation

The Analysis of Verbal Behavior - The primary focus of the present experiment was to study participants’ self-talk during conditional discrimination training and test for the emergence of...     

Austin Turk,stratification reinforcers and reversals,and traffic ticket decisions by Boston police during April and May of 2001

Austin Turk theorizes that places in stratification hierarchies affect police–citizen encounters by reinforcing or reversing the positional authority of police officers. To this point, however, there have been only three direct tests of the stratification portions of Turk's theory. Two examined overt conflict and all represented Turk's concepts using aggregated measures. The results are mixed. The present research adds a fourth test to the stratification and policing literatures by examining the effects of aggregated as well as disaggregated measures of stratification reinforcers and reversals on traffic ticket decisions by Boston police officers during April and May of 2001. Net of important control measures, there is no support for Turk's theory using the aggregated stratification measures employed in previous research. However, support for Turk's theory is clearly visible when viewed through the lens of some of the disaggregated stratification measures advanced for the first time in present research. We conclude that more research is needed to determine whether stratification reinforcers and reversals are among the factors affecting people in police blue and the people they police, with a sustained focus on both aggregated and disaggregated measures.     

Non-Normal Bivariate Distributions with Normal Marginals

The purpose of this note is to indicate that Fieller's Theorem can be expressed in the matrix formulation of the general linear model. The practical consequence is that one general computer program which can estimate the parameters and test the validity of a pertinent model, can also compute confidence limits for the ratios of any linear combinations of the parameters.     

Trend analysis with response incompatible formats and measurement error

The increasing popularity of longitudinal studies, along with the rapid advances in science and technology, has created a potential incompatibility between data formats, which leads to an inference problem when applying conventional statistical methods. This inference problem is further compounded by measurement error, since incompatible data format often arise in the context of measuring latent constructs. Without a systematic study of the impact of scale differences, ad-hoc approaches generally lead to inconsistent estimates and thus, invalid statistical inferences. In this paper, we examine the asymptotic properties and identify conditions that guarantee consistent estimation within the context of a trend analysis with response incompatible formats and measurement error. For model estimation, we introduce two competing methods that use a generalized estimating equation approach to provide inferences for the parameters of interest, and highlight the relative strengths of each method. The approach is illustrated by data obtained from a multi-centre AIDS cohort study (MACS), where a trend analysis of an immunologic marker of HIV infection is of interest.     

Modern variable selection for longitudinal semi-parametric models with missing data

Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.     

Lower bounds on the adaptivity gaps in variants of the stochastic knapsack problem

We consider stochastic variants of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary distributions. Items are placed in the knapsack sequentially, and the act of placing an item in the knapsack instantiates its size. The goal is to compute a policy for insertion of the items, that maximizes the expected value of the set of items placed in the knapsack. These variants that we study differ only in the formula for computing the value of the final solution obtained by the policy. We consider both nonadaptive policies (that designate a priori a fixed subset or permutation of items to insert) and adaptive policies (that can make dynamic decisions based on the instantiated sizes of the items placed in the knapsack thus far). Our work characterizes the benefit of adaptivity. For this purpose we use a measure called the adaptivity gap: the supremum over instances of the ratio between the expected value obtained by an optimal adaptive policy and the expected value obtained by an optimal non-adaptive policy. We show that while for the variants considered in the literature this quantity is bounded by a constant there are other variants where it is unbounded.