Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Common KIF6 Polymorphism Increases Vulnerability to Low-Density Lipoprotein Cholesterol: Two Meta-Analyses and a Meta-Regression Analysis

Background: We sought to determine if a common polymorphism can influence vulnerability to LDL cholesterol, and thereby influence the clinical benefit derived from therapies that reduce LDL cholesterol.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Development and application of a multiple linear regression model to consider the impact of weekly waste container capacity on the yield from kerbside recycling programmes

The paper describes the development and application of a multiple linear regression model to identify how the key elements of waste and recycling infrastructure, namely container capacity and frequency of collection affect the yield from municipal kerbside recycling programmes. The overall aim of the research was to gain an understanding of the factors affecting the yield from municipal kerbside recycling programmes in Scotland. The study isolates the principal kerbside collection service offered by 32 councils across Scotland, eliminating those recycling programmes associated with flatted properties or multi occupancies. The results of a regression analysis model has identified three principal factors which explain 80% of the variability in the average yield of the principal dry recyclate services: weekly residual waste capacity, number of materials collected and the weekly recycling capacity. The use of the model has been evaluated and recommendations made on ongoing methodological development and the use of the results in informing the design of kerbside recycling programmes. The authors hope that the research can provide insights for the ongoing development of methods to optimise the design and operation of kerbside recycling programmes.

The effects and side-effects of statistics education: Psychology students’ (mis-)conceptions of probability

In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists’ prediction who propose that education reduces misconceptions in general. They also predict that students with higher cognitive ability and higher need for cognition are less susceptible to biases. In Experiments 1 and 2 we found that the equiprobability bias increased with statistics education, and it was negatively correlated with students’ cognitive abilities. The representativeness heuristic was mostly unaffected by education, and it was also unrelated to cognitive abilities. In Experiment 3 we demonstrated through an instruction manipulation (by asking participants to think logically vs. rely on their intuitions) that the reason for these differences was that these biases originated in different cognitive processes.

Stress, Positive Psychology and the National Student Survey

The aim was to explore the predictive ability of sources of stress and a range of dispositional and coping behaviours on student satisfaction and motivation. Most research exploring sources of stress and coping in students construes stress as psychological distress, with little attempt to consider positive experiences of stress. A questionnaire was administered to 120 first-year UK psychology students. Questions were asked which measured sources of stress when rated as likely to contribute to distress (a hassle) and likely to help one achieve (an uplift). The sources of stress were amended from the UK National Student Survey (NSS, 2011). Support, control, self-efficacy, personality and coping style were also measured, along with their potential affect on
course satisfaction, motivation and feeling part of a learning community. The sources of stress likely to lead to distress were more often significant than sources of stress likely to lead to positive, eustress states. Ironically,
factors one would consider would help students, such as the university support facilities, only did so when rated as a hassle, not as an uplift. Published university league tables draw heavily on student course satisfaction but this negatively correlated with intellectual motivation and feeling part of a learning
community. This suggests course satisfaction alone reveals an incomplete picture of the student experience. Course educators need to consider how course experiences contribute not just to potential distress but to
potential eustress. Teaching quality, effective support and work-life balance are key to student satisfaction and motivation. How educators interact with their students and the opportunities they create in and outside the class to promote peer support are likely to enhance satisfaction and motivation.

Efficacy of a powered wheelchair simulator for school aged children: A randomised controlled trial.

Purpose: To determine the efficacy of a custom made wheelchair simulation in training children to use a powered wheelchair (PWC). Design: Randomised controlled trial employing the 4C/ID-model of learning. Twenty-eight typically developing children (13M, 15F; mean age 6 years, SD 6 months) were assessed on their operation of a PWC using a functional evaluation rating scale. Participants were randomly assigned to intervention (8x 30minute training sessions using a joystick operated wheelchair simulation) or control conditions (no task), and were re-assessed on their PWC use following the intervention phase. Additional data from the simulation on completion times, errors and total scores were recorded for the intervention group. Results: Analysis of variance showed a main effect of time, with planned comparisons revealing a statistically significant change in PWC use for the intervention (p = 0.022) but not the control condition. Whilst the intervention group showed greater improvement than the controls this did not reach statistical significance. Multiple regression analyses showed that gender was predictive of pre-test (p = 0.005) functional ability. Implications: A simulated wheelchair task appears to be effective in helping children learn to operate a PWC. Greater attention should be given to female learners who underperformed when compared to their male counterparts. This low cost intervention could be easily employed at home to reduce PWC training times in children with motor disorders.

A Randomized Controlled Trial of Interventions to Promote Adjustment in Children With Congenital Heart Disease Entering School and Their Families

Objective: To report on a randomized controlled trial of psychological interventions to promote adjustment in children with congenital heart disease and their families.
Method: Following baseline assessment, 90 children (aged 4–5 years) and their families were randomly assigned to an Intervention or Control group before entering school. 68 (76%) were retained at 10-month follow-up.
Results: Gains were observed on measures of maternal mental health and family functioning. Although no differences were found on measures of child behavior at home or school, children in the intervention group were perceived as “sick” less often by their mother and missed fewer days from school. A regression model, using baseline measures as predictors, highlighted the importance of maternal mental health, worry and child neurodevelopmental functioning for child behavioral outcomes almost a year later.
Conclusions: The intervention promoted clinically significant gains for the child and family. The program is of generalizable significance.