Tackling the human problems in planning

This article presents findings from the first phase of a research program on the management of long-range planning systems. The initial focus is on behavioral problems encountered by those using planning systems and solutions for dealing with them. Both the problems and solutions were identified by chief planning officers during a university sponsored workshop. Some observations are offered about the way in which managerial thought processes affect the potential for resolving complex behavioral problems in organizations.     

Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs

Objectives in many longitudinal studies of individuals infected with the human immunodeficiency virus (HIV) include the estimation of population average trajectories of HIV ribonucleic acid (RNA) over time and tests for differences in trajectory across subgroups. Special features that are often inherent in the underlying data include a tendency for some HIV RNA levels to be below an assay detection limit, and for individuals with high initial levels or high ranges of change to drop out of the study early because of illness or death. We develop a likelihood for the observed data that incorporates both of these features. Informative drop-outs are handled by means of an approach previously published by Schluchter. Using data from the HIV Epidemiology Research Study, we implement a maximum likelihood procedure to estimate initial HIV RNA levels and slopes within a population, compare these parameters across subgroups of HIV-infected women and illustrate the importance of appropriate treatment of left censoring and informative drop-outs. We also assess model assumptions and consider the prediction of random intercepts and slopes in this setting. The results suggest that marked bias in estimates of fixed effects, variance components and standard errors in the analysis of HIV RNA data might be avoided by the use of methods like those illustrated.     

Umvu estimators for the population mean and variance based on random effects models for lognormal data

Cross-classified data are often obtained in controlled experimental situations and in epidemiologic studies. As an example of the latter, occupational health studies sometimes require personal exposure measurements on a random sample of workers from one or more job groups, in one or more plant locations, on several different sampling dates. Because the marginal distributions of exposure data from such studies are generally right-skewed and well-approximated as lognormal, researchers in this area often consider the use of ANOVA models after a logarithmic transformation. While it is then of interest to estimate original-scale population parameters (e.g., the overall mean and variance), standard candidates such as maximum likelihood estimators (MLEs) can be unstable and highly biased. Uniformly minimum variance unbiased (UMVU) cstiniators offer a viable alternative, and are adaptable to sampling schemes that are typiral of experimental or epidemiologic studies. In this paper, we provide UMVU estimators for the mean and variance under two random effects ANOVA models for logtransformed data. We illustrate substantial mean squared error gains relative to the MLE when estimating the mean under a one-way classification. We illustrate that the results can readily be extended to encompass a useful class of purely random effects models, provided that the study data are balanced.     

Dynamic Capabilities: Current Debates and Future Directions

The field of dynamic capabilities has developed very rapidly over the last ten years. In this paper we discuss the evolution of the concept, and identify two major current debates around the nature of dynamic capabilities and their consequences. We then review recent progress as background to identifying the contributions of the seven papers in this special issue, and discuss the relative merits of qualitative and quantitative studies for investigating dynamic capabilities. We conclude with recommendations for future research arguing for more longitudinal studies which can examine the processes of dynamic abilities over time, and for studies in diverse industries and national contexts.     

Dynamic Capabilities: Current Debates and Future Directions

The field of dynamic capabilities has developed very rapidly over the last ten years. In this paper we discuss the evolution of the concept, and identify two major current debates around the nature of dynamic capabilities and their consequences. We then review recent progress as background to identifying the contributions of the seven papers in this special issue, and discuss the relative merits of qualitative and quantitative studies for investigating dynamic capabilities. We conclude with recommendations for future research arguing for more longitudinal studies which can examine the processes of dynamic abilities over time, and for studies in diverse industries and national contexts.     

Organizational Learning,knowledge creation,problem formulation and innovation in messy problems

This paper attempts to bring the discussion about knowledge creation, innovation and organizational learning to a level that addresses how messy problems are addressed and how the organizations must integrate the viewpoints of the key decision-makers, establish a process for testing their assumptions, include the context including the environment design, and to allow innovation and creativity to enter the choice of actions. It builds on the literature of problem formulation, innovation, and experimentation and on the articles in this special issue.     

The likelihood approach for the comparison of medical diagnostic system with multiple binary tests

Detection (diagnosis) techniques play an important role in clinical medicine. Early detection of diseases could be life-saving, and the consequences of false-positives and false-negatives could be costly. Using multiple measurements strategy is a popular tool to increase diagnostic accuracy. In addition to the new diagnostic technology, recent advances in genomics, proteomics, and other areas have allowed some of these newly developed individual biomarkers measured by non-invasive and inexpensive procedures (e.g. samples from serum, urine or stool) to progress from basic discovery research to assay development. As more tests become commercially available, there is an increasing interest for clinicians to request combinations of various non-invasive and inexpensive tests to increase diagnostic accuracy. Using information regarding individual test sensitivities and specificities, we proposed a likelihood approach to combine individual test results and to approximate or estimate the combined sensitivities and specificities of various tests taking into account the conditional correlations to quantify system performance. To illustrate this approach, we considered an example using various combinations of diagnostic tests to detect bladder cancer.     

Reducing Bias and Mean Squared Error Associated With Regression-Based Odds Ratio Estimators

Ratio estimators of effect are ordinarily obtained by exponentiating maximum-likelihood estimators (MLEs) of log-linear or logistic regression coefficients. These estimators can display marked positive finite-sample bias, however. We propose a simple correction that removes a substantial portion of the bias due to exponentiation. By combining this correction with bias correction on the log scale, we demonstrate that one achieves complete removal of second-order bias in odds ratio estimators in important special cases. We show how this approach extends to address bias in odds or risk ratio estimators in many common regression settings. We also propose a class of estimators that provide reduced mean bias and squared error, while allowing the investigator to control the risk of underestimating the true ratio parameter. We present simulation studies in which the proposed estimators are shown to exhibit considerable reduction in bias, variance, and mean squared error compared to MLEs. Bootstrapping provides further improvement, including narrower confidence intervals without sacrificing coverage.