Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials

To be useful to clinicians, prognostic and diagnostic indices must be derived from accurate models developed by using appropriate data sets. We show that fractional polynomials, which extend ordinary polynomials by including non-positive and fractional powers, may be used as the basis of such models. We describe how to fit fractional polynomials in several continuous covariates simultaneously, and we propose ways of ensuring that the resulting models are parsimonious and consistent with basic medical knowledge. The methods are applied to two breast cancer data sets, one from a prognostic factors study in patients with positive lymph nodes and the other from a study to diagnose malignant or benign tumours by using colour Doppler blood flow mapping. We investigate the problems of biased parameter estimates in the final model and overfitting using cross-validation calibration to estimate shrinkage factors. We adopt bootstrap resampling to assess model stability. We compare our new approach with conventional modelling methods which apply stepwise variables selection to categorized covariates. We conclude that fractional polynomial methodology can be very successful in generating simple and appropriate models.     

Approximating the Shapiro-Wilk W-test for non-normality

A new approximation for the coefficients required to calculate the Shapiro-WilkW-test is derived. It is easy to calculate and applies for any sample size greater than 3. A normalizing transformation for theW statistic is given, enabling itsP-value to be computed simply. The distribution of the new approximation toW agrees well with published critical points which use exact coefficients.     

Exploring Politicized Trust in a Racially Diverse Computer Science Classroom

ABSTRACT

Over the past decade, abroad range of policymakers, corporations, educators, and scholars in the United States have catapulted computer science (CS) education from the sidelines to the center of K-12 public education discourse. While calls for CS education are often framed in terms of national and economic competitiveness, there is a growing interest amongst equity scholars in curricular interventions that directly engage the ethical and sociopolitical issues surrounding CS and its role in society. Yet, less attention has been given to how sociocultural dynamics of classrooms play a mediating role in these spaces. Drawing on video data from an equity-oriented CS classroom, we argue aconflict that arose during design activities was rooted in alack of trust and solidarity between students. Ultimately, we make the case that in addition to curricular innovations, equity efforts in CS education must prioritize the cultivation of positive student relationships.     

Regionalizing Healthcare in Alberta: Legislated Change, Uncertainty and Loose Coupling

In this paper,we examine the re-organizing attempts of the Alberta government in healthcare from the viewpoint of uncertainty,loose coupling and the frequently unrecognized consequences of such change.We suggest that our understanding of change processes can be enhanced by conceptualizing the impact of task and environmental uncertainty through the nature of coupling between organizational elements.Based on our .ndings,we propose that the greater the degree of loose coupling in an organization,the more di .cult change is likely to be,and the more likely the occurrence of unanticipated consequences.     

Do makerspaces represent scalable production models of community-based redistributed manufacturing?

Abstract

This research explores the development of local community-based ‘makerspaces’ as potential scalable forms of redistributed manufacturing (RDM). Makerspaces are rapidly emerging in post-industrial economies and have been identified as a catalyst of local regeneration in urban areas. However, their role in local production systems is limited. There is a gap in the literature, with respect to the evolution of makerspaces and their productive contribution. The purpose of this paper therefore is to identify, classify and examine the different types of makerspaces. Our focus is on the implementation characteristics that enable industrial production activity to take place. First, we used Leximancer (to identify from the literature) three types of makerspace. Second, we then identify five RDM implementation characteristics. The characteristics were integrated together to form the RDM-makerspace implementation model. Third, case studies were purposively selected to test and advance this model. They were subsequently classified as a Type 1 (educational), Type 2 (design) or Type 3 (production) makerspace. Only one of the case studies was classified as a fully evolved Type 3 production space. The findings concur with the literature that makerspaces tend to be primarily Type 1 or Type 2. Finally, the contribution to local production theory is emphasized.     

A Comparison of Statistical Methods for Age-related Reference Intervals

Age-specific reference intervals are commonly used in the routine monitoring of individuals, where interest lies in the detection of extreme values, possibly indicating abnormality. Here, a review is given of the wide range of statistical techniques which have been proposed for the construction of these intervals and issues such as the estimation of confidence bands and goodness of fit are discussed. Three methods, thought to be the most widely applied approaches, are considered in more detail. Comparisons are made on the basis of reference interval estimation for three real data sets.     

A method for estimating age-specific reference intervals ('normal ranges') based on fractional polynomials and exponential transformation

The age-specific reference interval is an important screening tool in medicine. Put crudely, an individual whose value of a variable of interest lies outside certain extreme centiles may be suspected of abnormality. We propose a parametric method for constructing such intervals. It provides smooth centile curves and explicit formulae for the centile estimates and for standard deviation (SD) scores (age-standardized values). Each parameter of an exponential–normal or modulus–exponential–normal density is modelled as a fractional polynomial function of age. Estimation is by maximum likelihood. These three- and four-parameter models involve transformations of the data towards normality which remove non-normal skewness and/or kurtosis. Fractional polynomials provide more flexible curve shapes than do conventional polynomials. The method easily accommodates binary covariates facilitating, for example, parsimonious modelling of age- and sex-specific centile curves. A method of calculating precision profiles for centile estimates is proposed. Goodness of fit is assessed by using Q–Q -plots and Shapiro–Wilk W -tests of the SD scores, and likelihood ratio tests of the parameters of an enlarged model. Four substantial real data sets are used to illustrate the method. Comparisons are made with the semiparametric LMS method of Cole and Green.