Strategic alliances in fast-moving markets

The pace of technological change and market shifts is forcing organizations to reconsider their strategies for creating sustainable competitive advantage. Strategic alliances focused on destabilizing market values are currently the most effective source of sustainable competitive advantage. The purpose of this article is to explain how successful strategic alliances can be arranged in fast-moving markets.     

Problem-based Learning: Potential and Implementation Issues

This study addresses the issues associated with the application of problem-based learning, examines the key design dimensions of a problem-based learning course and considers the implications of problem-based learning for learners and educators.     

Impact of Stratification Imbalance on Probability of Type I Error

The impact of ignoring the stratification effect on the probability of a Type I error is investigated. The evaluation is in a clinical setting where the treatments may have different response rates among the strata. Deviation from the nominal probability of a Type I error, α, depends on the stratification imbalance and the heterogeneity in the response rates; it appears that the latter has a larger impact. The probability of a Type I error is depicted for cases in which the heterogeneity in the response rate is present but there is no stratification imbalance. Three-dimensional graphs are used to demonstrate the simultaneous impact of heterogeneity in response rates and of stratification imbalance.     

Comparison between Bayesian approach and frequentist methods for estimating relative risk in randomized controlled trials: a simulation study

Relative risks (RRs) are often considered as preferred measures of association in randomized controlled trials especially when the binary outcome of interest is common. To directly estimate RRs, log-binomial regression has been recommended. Although log-binomial regression is a special case of generalized linear models, it does not respect the natural parameter constraints, and maximum likelihood estimation is often subject to numerical instability that leads to convergence problems. Alternative methods for solving log-binomial regression convergence problems have been proposed. A Bayesian approach also was introduced, but the comparison between this method and frequentist methods has not been fully explored. We compared five frequentist and one Bayesian methods for estimating RRs under a variety of scenario. Based on our simulation study, there is not a method that can perform well based on different statistical properties, but COPY 1000 and modified log-Poisson regression can be considered in practice.     

Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old.     

An adaptive algorithm for active queue management

This paper proposes an adaptive proportional integral (API) algorithm for active queue management. API uses mean packet loss ratio and mean queue length as control error to adaptively adjust packet drop probability in order to expedite congestion control. The extensive simulations validate that API achieves faster control response and improved performance in terms of goodput, average queue length, and packet loss ratio.