Bringing baby home: a comprehensive case management program for obstetrical services

Originally presented to the Society of Home Health Care Management of the American College of Physician Executives at its November 16, 1993, meeting in Tucson, Ariz., the program described in this article was the winner of the College's 1994 Innovations Award in Medical Quality Management, sponsored by Merck Sharp & Dohme. The program shows the potential of case management for both improvement in the quality of care and containment of costs for a managed care population with a substantial Medicaid segment. This article is part of a continuing series on innovative programs in home health care.     

A note on the estimation of selective values from recaptures of marked animals when selection pressures remain constant over time

Summary An equation is given for the estimation of selective values from data obtained by mark-recapture experiments, assuming that selective pressures remain constant while the experiments are carried out. The equation does not have an explicit solution but can readily be solved using a trial-and-error method. The use of the equation is illustrated on some data reported byKettlewell et al. (1969) from an experiment involvingtypica andedda morphs of the mothAmathes glareosa. It is found that theedda morph apparently had a selective advantage of about 12% per day compared to thetypica morph and that this is significantly different from zero. Using another methodKettlewell concluded that the selective advantage of theedda morph was only 7% and that this was not statistically significant.     

THE USE OF ARCS AND NODES FOR THE DETERMINATION OF CRITICAL PATHS IN PERT/CPM NETWORKS

The critical path in a PERT/CPM network is determined in various textbooks by finding the path with associated minimal slack for the network arcs or by finding the path with associated minimal slack for the network nodes. This paper points out that the use of nodes can result in a network path being incorrectly identified as a path of maximum length.     

DETERMINING OPTIMAL SHIFT SCHEDULES FOR TELEPHONE TRAFFIC EXCHANGE OPERATORS

A branch and bound algorithm is presented for determining the minimum number of telephone operators, and their shift schedules, required to meet demand that varies over a 24 hour operating period. An integer linear programming formulation is used, and the algorithm is described in terms of its separation, relaxation, fathoming, and branching procedures. Computational results are provided, using actual operating data. The results indicate that practical sized problems can be solved by the algorithm, involving as many as 100 different shift type variables and demand profiles typical of those encountered in many telephone traffic exchanges.     

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.     

Bayesian inference for dynamic social network data

We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations.     

Retracted and replaced: Sodium cromoglicate: an ineffective drug or meta‐analysis misused?

The following article from Pharmaceutical Statistics, Sodium cromoglicate: an ineffective drug or meta‐analysis misused? by M. T. Stevens, A. M. Edwards, J. B. L. Howell published online on 15 March 2007 in Wiley InterScience ( www.interscience.wiley.com ), has been retracted by agreement between the author, the journal Editor in Chief, Steven Julious, and John Wiley & Sons, Ltd. The retraction has been agreed because the article is not yet ready for publication and an early version without revisions was published in error. Replacement article pending. Copyright © 2007 John Wiley & Sons, Ltd.