Large sample inference in random coefficient regression models

Random coefficient regression models have been used t odescribe repeated measures on members of a sample of n in dividuals . Previous researchers have proposed methods of estimating the mean parameters of such models. Their methods require that eachindividual be observed under the same settings of independent variablesor , lesss stringently , that the number of observations ,r , on each individual be the same. Under the latter restriction ,estimators of mean regression parameters exist which are consist ent as both r^→∞and n^→∞ and efficient as r^→∞, and large sample ( r large ) tests of mean parameters are available . These results are easily extended to the case where not a11 individuals are observed an equal number of times provided limit are taken as min(r) → ∞. Existing methods of inference , however, are not justified by the current literature when n is large and r is small, as is the case i n many bio-medical applications . The primary con tribution of the current paper is a derivation of the asymptotic properties of modifications of existing estimators as n alone tends to infinity, r fixed. From these properties it is shown that existing methods of inference, which are currently justified only when min(r) is large, are also justifiable when n is large and min(r) is small. A secondary contribution is the definition of a positive definite estimator of the covariance matrix for the random coefficients in these models. Use of this estimator avoids computational problems that can otherwise arise.     

Biostatistics Instruction at Meharry Medical College

In a variety of settings, it is desirable to display a collection of likelihoods over a common interval. One approach is simply to superimpose the likelihood curves. However, where there are more than a handful of curves, such displays are extremely difficult to decipher. An alternative is simply to display a point estimate with a confidence interval, corresponding to each likelihood. However, these may be inadequate when the likelihood is not approximately normal, as can occur with small sample sizes or nonlinear models. A second dimension is needed to gauge the relative plausibility of different parameter values. We introduce the raindrop plot, a shaded figure over the range of parameter values having log-likelihood greater than some cutoff, with height varying proportional to the difference between the log-likelihood and the cutoff. In the case of a normal likelihood, this produces a reflected parabola so that deviations from normality can be easily detected. An analogue of the raindrop plot can also be used to display estimated random effect distributions, posterior distributions, and predictive distributions.     

Reducing Boarding in a Post‐Anesthesia Care Unit

When operating room schedules in hospitals are produced, the constraints and preferences of surgeons and hospital workers are a primary consideration. The downstream impact on post‐operative bed availability is often ignored. This can lead to the boarding of patients overnight in the post‐anesthesia care unit (PACU) because intensive care unit beds are unavailable. In this paper, we apply integer programming and simulation to develop improved surgical scheduling assignments. We want to balance new surgeries with hospital discharges in order to reduce the variability of occupied beds from one day to the next and, as a result, to reduce boarding in the PACU.     

Staff experience of a new approach to family safeguarding in Oxfordshire Children's Social Care Services

This paper presents the findings from a qualitative study that sought to understand the experiences of frontline staff working in Oxfordshire County Council (OCC) Children's Social Care Services and their views on a new family safeguarding model (Family Solutions Plus). Focus group interviews were conducted with 20 frontline staff and managers in different teams across OCC Children's Social Care Services using video conferencing software. Thematic analysis identified three overarching themes: Preparation for the implementation of Family Solutions Plus, staff views on the implemented model, and challenges to its implementation. Staff voiced strong support for the new model, which places a much greater emphasis than previous practice on supporting the whole family, developing parenting skills and keeping children safe with their families. The challenges associated with the transition to a new model were considerable in the short term, partly due to the COVID-19 pandemic, but there was optimism that the new model could be sustained and stabilized over time.     

A Transaction‐Efficiency Analysis of an Internet Retailing Supply Chain in the Music CD Industry*

The emergence of the Internet may have fundamentally altered the mechanisms underlying information exchanges between sellers and end consumers. However, little attention has been given to the impact these mechanisms have on the efficiency of supply chain operations. This paper begins to address this deficiency in the literature by evaluating supply chain transaction efficiency effects from Internet purchases by consumers. It develops and empirically tests a theoretical framework examining the role Internet purchases have in establishing transaction‐efficiency levels in product exchanges involving sellers, placed at different supply chain echelons, and consumers. The theoretical framework integrates the transaction‐cost and internet economics, inter‐organizational information systems, and supply chain management literatures. Empirical testing, via structural equation modeling, is based on archival data in the Internet music CD market. The results show that Internet‐mediated purchases by consumers allow for greater transaction efficiencies when inventory ownership is postponed farther upstream in the supply chain and supply chain echelons are disintermediated. The results also indicate that channel structure configuration, defined by the supply chains' Internet retailing echelon, moderates these transaction efficiency effects.