Parametric Modeling for Survival with Competing Risks and Masked Failure Causes

We consider a life testing situation in which systems are subject to failure from independent competing risks. Following a failure, immediate (stage-1) procedures are used in an attempt to reach a definitive diagnosis. If these procedures fail to result in a diagnosis, this phenomenon is called masking. Stage-2 procedures, such as failure analysis or autopsy, provide definitive diagnosis for a sample of the masked cases. We show how stage-1 and stage-2 information can be combined to provide statistical inference about (a) survival functions of the individual risks, (b) the proportions of failures associated with individual risks and (c) probability, for a specified masked case, that each of the masked competing risks is responsible for the failure. Our development is based on parametric distributional assumptions and the special case for which the failure times for the competing risks have a Weibull distribution is discussed in detail.     

Evaluation of an upper extremity student-role functioning scale using item response theory

Millions of workers suffer from upper extremity (musculoskeletal) disorders. Many of these workers are predisposed to upper extremity musculoskeletal disorders because of early exposure to ergonomic risks as students. Computer usage for four or more hours remains the greatest risk for upper extremity musculoskeletal disorders for workers and students alike. Developing preventative methods to reduce student exposure, and thus protect future workers from upper extremity musculoskeletal disorders later in life, requires an appropriate measure for upper extremity musculoskeletal disorder prevalence and related limitations for student functioning. Item response theory analysis was used to evaluate and further develop a upper extremity functioning scale for the student role.     

Introduction: moving youth participation forward

Given the emerging interest among researchers, practitioners, and policymakers in youth participation, it is important to examine and assess carefully the promise and challenges of youth engagement.     

Insider ownership and corporate performance: evidence from Germany

In this paper we address the question whether insider ownership affects corporate performance. Evidence from studies dealing with Anglo-Saxon countries is rather inconclusive, especially because results seem to be significantly affected by endogeneity. Economically, this is due to the fact that in these countries insider ownership seems to be mainly driven by management’s compensation contracts. We argue that Germany is different in this regard, as insider ownership is often related to family control, stock-based compensation is less widespread, and the market for corporate control used to be less developed. Starting from this presumption, our data allows an unbiased observation as to whether insider ownership affects firm performance. Using a pooled data set of 648 firm observations for the years 2003 and 1998, we find evidence for a positive and significant relationship between corporate performance—as measured by stock price performance, market-to-book ratio and return on assets—and insider ownership. This relationship seems to be rather robust, even if we account for potential endogeneity by applying a 2SLS regression approach. Furthermore, the results hold for a sub-sample of firms that did not have a stock-based compensation program in place. Moreover, we find outside block ownership as well as more concentrated insider ownership to have a positive impact on corporate performance. Overall, the results indicate that ownership structure might be an important variable explaining the long term value creation in the corporate sector.

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Federal budget helps drive health policy. A close-up look at the budget process and how it affects health care funding

Crafting the budget is an annual process that Congress and the administration use to define the national policy agenda. It is a massive undertaking to decide how more than two trillion dollars is spent by the federal government. Over 20 percent of the budget is used for health care. The budget is a political document that defines the priorities of the federal government and directly influences overall health policy. Its construction deserves to be followed and influenced by physicians to ensure the health of the public.     

The influence of top management corporate networks on CEO succession

We analyze empirically how supervisory board members with multiple directorships affect the decision to hire an inside or outside CEO successor. While a growing number of both theoretical and empirical studies analyze the influence of corporate performance and size or the ownership structure on this decision, the role of multiple board memberships within the CEO recruitment process has been widely neglected so far. The present study is based on panel data of the largest German companies covering the period from 1996 to 2008. Applying competing risk estimations we find a weak and positive association between the number of external directorships of the supervisory board members and internal CEO replacements. Distinguishing between different groups of external board positions, we find that external executives on the supervisory board increase the likelihood of external CEO replacements. In line with empirical findings for the US we argue that external executives improve the assessment of potential CEO successors leading to more outside CEO replacements. In contrast, we find evidence that external supervisory board mandates of the supervisory board members cause more internal CEO replacements. This finding indicates a substitution of external expertise of the executives by multiple supervisory board mandates but could also reflect a reduction of the monitoring intensity of the supervisory board.     

An Empirical Investigation of Terrorism-induced Stress on Expatriate Attitudes and Performance

Despite international terrorism's increasing relevance for international business, the effects of terrorism that confront employees during assignments abroad have hardly been investigated. Applying a stress perspective, this article analyzes the impact of terrorism-induced stress on attitudes and the performance of expatriates. Employing data from 143 expatriate managers in high-risk countries, the study shows that several terrorism-related stressors create a significant stress level for the individual, causing negative work attitudes and attitudes towards host country nationals (disaffection). This eventually leads to worse performance. We applied partial least squares (PLS) structural equation modeling (SEM) to analyze the entire impact path and found substantial support for our hypotheses. Of all the relevant stressors, intra-family conflicts due to terrorism have the greatest impact.     

Forecasting mortality rates via density ratio modeling

The authors propose a semiparametric approach to modeling and forecasting age‐specific mortality in the United States. Their method is based on an extension of a class of semiparametric models to time series. It combines information from several time series and estimates the predictive distribution conditional on past data. The conditional expectation, which is the most commonly used predictor in practice, is the first moment of this distribution. The authors compare their method to that of Lee and Carter.