Leader-employee congruence of expected contributions in the employee-organization relationship

Employees' expected contributions can be incongruent with those of their leader. We examine the congruence effect of leaders' and employees' expected contributions on job satisfaction. Results of cross-level polynomial regressions on 947 employees and 224 leaders support the congruence effect. When expected contributions are congruent, employees are more satisfied with their job. Our findings suggest that employees enjoy high challenges, as long as these challenges are in harmony with the expected contributions of their leaders. Employees are less satisfied with their jobs both when their expected contributions were higher than their leaders' and when their expected contributions were lower than those of their leaders. Beyond the relevance of having high expected contributions, the findings highlight the crucial role played by the congruence of expected contributions of leaders and employees.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

The additive hazards model with high-dimensional regressors

This paper considers estimation and prediction in the Aalen additive hazards model in the case where the covariate vector is high-dimensional such as gene expression measurements. Some form of dimension reduction of the covariate space is needed to obtain useful statistical analyses. We study the partial least squares regression method. It turns out that it is naturally adapted to this setting via the so-called Krylov sequence. The resulting PLS estimator is shown to be consistent provided that the number of terms included is taken to be equal to the number of relevant components in the regression model. A standard PLS algorithm can also be constructed, but it turns out that the resulting predictor can only be related to the original covariates via time-dependent coefficients. The methods are applied to a breast cancer data set with gene expression recordings and to the well known primary biliary cirrhosis clinical data.     

Missing item imputation for quality-of-life instruments with application to asthma quality-of-life questionnaires

There has been increasing use of quality-of-life (QoL) instruments in drug development. Missing item values often occur in QoL data. A common approach to solve this problem is to impute the missing values before scoring. Several imputation procedures, such as imputing with the most correlated item and imputing with a row/column model or an item response model, have been proposed. We examine these procedures using data from two clinical trials, in which the original asthma quality-of-life questionnaire (AQLQ) and the miniAQLQ were used. We propose two modifications to existing procedures: truncating the imputed values to eliminate outliers and using the proportional odds model as the item response model for imputation. We also propose a novel imputation method based on a semi-parametric beta regression so that the imputed value is always in the correct range and illustrate how this approach can easily be implemented in commonly used statistical software. To compare these approaches, we deleted 5% of item values in the data according to three different missingness mechanisms, imputed them using these approaches and compared the imputed values with the true values. Our comparison showed that the row/column-model-based imputation with truncation generally performed better, whereas our new approach had better performance under a number scenarios.     

Parametric non-mixture cure models for schedule finding of therapeutic agents

We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non-mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non-mixture cure model over the existing model.     

Measuring people's trust

Summary. We measure trust and trustworthiness in British society with a newly designed experiment using real monetary rewards and a sample of the British population. The study also asks the typical survey question that aims to measure trust, showing that it does not predict 'trust' as measured in the experiment. Overall, about 40% of people were willing to trust a stranger in our experiment, and their trust was rewarded half of the time. Analysis of variation in the trust behaviour in our survey suggests that trusting is more likely if people are older, their financial situation is either 'comfortable' or 'difficult' compared with 'doing alright' or 'just getting by', they are a homeowner or they are divorced, separated or never married compared with those who are married or cohabiting. Trustworthiness also is more likely among subjects who are divorced or separated relative to those who are married or cohabiting, and less likely among subjects who perceive their financial situation as 'just getting by' or 'difficult'. We also analyse the effect of attitudes towards risks on trust.     

Toward a teacher solidarity lens: former teachers of color (re)envisioning educational research

Based on a two-year self-study by a group of early-career scholars of color, we explore and purposefully name our role, within the contemporary context of neoliberal reform, as educational researchers of color who are former K-12 teachers. We capture the insights that emerged from our self-study through a close reading of dominant neoliberal educational reform discourses, particularly through an examination of the writings of Michelle Rhee and Wendy Kopp. Along three dimensions of: (1) experience as teachers; (2) solidarity with teachers; and (3) analyses of racism in schooling, we characterize prominent discourses through which educators, researchers, and the public describe teachers and teaching. We name these discursive frames to make explicit the assumptions that are embedded in each and the intentional or inadvertent consequences of each. Finally, we propose a teacher solidarity lens through which we strive to approach our research and work with teachers.     

A flexible semiparametric transformation model for survival data

I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested.     

Demography and housing demand—what can we learn from residential construction data?

There are obvious reasons why residential construction should depend on the population’s age structure. We estimate this relation on Swedish time series data and Organization for Economic Cooperation and Development panel data. Large groups of young adults are associated with higher rates of residential construction, but there is also a significant negative effect from those above 75. Age effects on residential investment are robust and forecast well out-of-sample in contrast to the corresponding house price results. This may explain why the debate around house prices and demography has been rather inconclusive. Rapidly aging populations in the industrialized world makes the future look bleak for the construction industry.