Strategic technology alliance termination: An empirical investigation

There is growing consensus that overall alliance termination rates are high. However, despite this track record of termination and despite unsurpassed growth rates of strategic technology alliances, little is known about the reasons for their termination. Typically strategic alliances have been characterized as inherently instable, i.e. often involving unplanned and premature termination of the alliance by partnering firms indicating alliance failure. The literature on strategic technology alliances, however, proposes that alliance termination does not always indicate failure, but can be intended and can be a sign of strength. We examine these different perceptions by using a sample of 48 strategic technology alliances in different high-technology industries. The findings in the paper confirm that the rates of termination are rather high for strategic technology alliances. Overall, we found that in particular negative prospects about future cooperation, negative perceptions about joint benefits and the lack of a win-win situation had an impact on the decision to terminate a strategic technology alliance. Also, the fact that some companies opt only for short-term (not for long-term and renewed) cooperation seems to introduce a negative factor into the longevity of strategic technology alliances.     

Strategies for handling missing data in longitudinal studies with questionnaires

Missing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias.     

Handling of missing data in long‐term clinical trials: a case study

Missing data in clinical trials is a well‐known problem, and the classical statistical methods used can be overly simple. This case study shows how well‐established missing data theory can be applied to efficacy data collected in a long‐term open‐label trial with a discontinuation rate of almost 50%. Satisfaction with treatment in chronically constipated patients was the efficacy measure assessed at baseline and every 3 months postbaseline. The improvement in treatment satisfaction from baseline was originally analyzed with a paired t‐test ignoring missing data and discarding the correlation structure of the longitudinal data. As the original analysis started from missing completely at random assumptions regarding the missing data process, the satisfaction data were re‐examined, and several missing at random (MAR) and missing not at random (MNAR) techniques resulted in adjusted estimate for the improvement in satisfaction over 12 months. Throughout the different sensitivity analyses, the effect sizes remained significant and clinically relevant. Thus, even for an open‐label trial design, sensitivity analysis, with different assumptions for the nature of dropouts (MAR or MNAR) and with different classes of models (selection, pattern‐mixture, or multiple imputation models), has been found useful and provides evidence towards the robustness of the original analyses; additional sensitivity analyses could be undertaken to further qualify robustness. Copyright © 2012 John Wiley & Sons, Ltd.     

Flexible estimation of serial correlation in nonlinear mixed models

In the conventional linear mixed-effects model, four structures can be distinguished: fixed effects, random effects, measurement error and serial correlation. The latter captures the phenomenon that the correlation structure within a subject depends on the time lag between two measurements. While the general linear mixed model is rather flexible, the need has arisen to further increase flexibility. In addition to work done in the area, we propose the use of spline-based modeling of the serial correlation function, so as to allow for additional flexibility. This approach is applied to data from a pre-clinical experiment in dementia which studied the eating and drinking behavior in mice.     

An analytical management framework for new product development processes featuring uncertain iterations

This paper presents an analytical framework for effective management of projects with uncertain iterations. The framework is based upon: (1) the combination of two complementary techniques, one focused on improving iterative process architectures, the Design Structure Matrix, and one focused on predicting project performance, the Graphical Evaluation Review Technique; and (2) the introduction of an activity set-based criticality measure. The intent of the framework is to help project managers and researchers identify and evaluate alternative process architectures, in order to help them determine the alternative which best balances risk and other project performance parameters, as illustrated through an example application.     

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

Summary.  A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results.     

Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure

Quantitative risk assessment involves the determination of a safe level of exposure. Recent techniques use the estimated dose-response curve to estimate such a safe dose level. Although such methods have attractive features, a low-dose extrapolation is highly dependent on the model choice. Fractional polynomials, basically being a set of (generalized) linear models, are a nice extension of classical polynomials, providing the necessary flexibility to estimate the dose-response curve. Typically, one selects the best-fitting model in this set of polynomials and proceeds as if no model selection were carried out. We show that model averaging using a set of fractional polynomials reduces bias and has better precision in estimating a safe level of exposure (say, the benchmark dose), as compared to an estimator from the selected best model. To estimate a lower limit of this benchmark dose, an approximation of the variance of the model-averaged estimator, as proposed by Burnham and Anderson, can be used. However, this is a conservative method, often resulting in unrealistically low safe doses. Therefore, a bootstrap-based method to more accurately estimate the variance of the model averaged parameter is proposed.     

A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

Multiple imputation (MI) is now a reference solution for handling missing data. The default method for MI is the Multivariate Normal Imputation (MNI) algorithm that is based on the multivariate normal distribution. In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable. This simulation study compares the performance of the MNI and ordinal imputation regression model for incomplete longitudinal ordinal data for situations covering various numbers of categories of the ordinal outcome, time occasions, sample sizes, rates of missingness, well-balanced, and skewed data.     

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated. Copyright © 2014 John Wiley & Sons, Ltd.     

Teacher ethnicity,student ethnicity,and student outcomes

A review of the empirical literature was conducted to establish the relation between teacher and student ethnicity, and cognitive and noncognitive student outcomes. It was hypothesized that ethnic teacher–student congruence results in more favorable outcomes for especially minority students. A total of 24 quantitative studies focusing on primary and secondary education in the United States were reviewed. The results show that there is as yet little unambiguous empirical evidence that a stronger degree of ethnic match be it in the form of a one-to-one coupling of a teacher to students with the same ethnic background, or a larger share of ethnic minority teachers at an ethnically mixed school, leads to predominantly positive results. Insofar positive effects were found, they apply to a greater extent to subjective teacher evaluations than to objective achievement outcome measures.