Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.     

A Passion for Gambling: A Generation-Specific Conceptual Analysis and Review of Gambling Among Older Adults in Canada

The proliferation of gambling opportunities in Canada, coupled with an aging population, has led to an increased prevalence of gambling among older adults. Encouraged by this trend, gambling industries have modified their activities to attract and market to this group. Yet, older adults are not a homogeneous group. The life experiences, values, and attitudes shared by generations make a cohort-specific analysis of gambling among older adults a worthwhile pursuit. Drawing from the Dualistic Model of Passion (Vallerand et al. in J Pers Soc Psychol 85(4):756–767, 2003), we discuss the role of passion in shaping gambling behaviours, and the implications of a harmonious or obsessive passion on the benefits and risks to two distinct generations of older adults. Based on their generational attributes, we posit that members of the Silent Generation (those born between 1925 and 1942) stand to gain more from the benefits of recreational gambling, but also stand lose more from problem gambling, than their children’s generation, the Baby Boomers (those born between 1942 and 1964). Preventative strategies to assist problem gambling seniors, along with recommendations for further research, are discussed.     

Disappointment without prior expectation: a unifying perspective on decision under risk

The central idea of Disappointment theory is that an individual forms an expectation about a risky alternative, and may experience disappointment if the outcome eventually obtained falls short of the expectation. We abandon the hypothesis of a well-defined prior expectation: disappointment feelings may arise from comparing the outcome received with anyof the gamble’s outcomes that the individual failed to get. This leads to a new, general form of Disappointment model. It encompasses Rank Dependent Utility with an explicit one-parameter probability transformation, and Risk-Value models with a generic risk measure including Variance, providing a unifying behavioral foundation for these models. JEL Classification D80 . D81     

Survival Trees: An Alternative Non-Parametric Multivariate Technique for Life History Analysis

In this paper an extension of tree-structured methodology to cover censored survival analysis is discussed. Tree-based methods (also called recursive partitioning) provide a useful alternative to the classical survival data analysis techniques, such as the semi-parametric model of Cox, whenever the main purpose is defining groups of individuals, either with complete or censored life history, having different survival probability, based on the values of selected covariates. The essential feature of recursive partitioning is the construction of a decision rule in the form of a binary tree. Trees generally require fewer assumptions than classical methods and handle non standard and non linear data structures efficiently. Tree-growing methods make the processes of covariate selection and grouping of categories in event history models explicit. An example concerning the analysis of time to marriage of Italian women is presented.     

Mixing patterns between age groups in social networks

We present a method for estimating transmission matrices that describe the mixing and the probability of infection between age groups. Transmission matrices can be used to estimate age-dependent forces of infection in age-structured, compartmental models for the study of infectious diseases. We analyze the social network generated by the synthetic population of Portland and extract mixing patterns. Our results show that the mixing within the population consists of two groups, children and adults. Children interact most frequently with other children close to their own age, while adults interact with a wider range of age groups and the durations of typical adult contacts are shorter than typical contacts between children. Furthermore, the transmission matrix shows that children are more likely to acquire infection than adults.     

A new 'biased coin design' for the sequential allocation of two treatments

Summary.  Efron's biased coin design is a well-known randomization technique that helps to neutralize selection bias in sequential clinical trials for comparing treatments, while keeping the experiment fairly balanced. Extensions of the biased coin design have been proposed by several researchers who have focused mainly on the large sample properties of their designs. We modify Efron's procedure by introducing an adjustable biased coin design, which is more flexible than his. We compare it with other existing coin designs; in terms of balance and lack of predictability, its performance for small samples appears in many cases to be an improvement with respect to the other sequential randomized allocation procedures.     

Perceptions and expectations of price changes and inflation: A review and conceptual framework

Drawing on relevant literature from a diverse range of academic disciplines we present a conceptual framework intended to further our understanding of perceptions and expectations of price changes and inflation. Based on this framework, we provide a detailed review of the literature and an analysis of open issues in current research. The review is primarily concerned with individuals’ perceptions and expectations of price changes and inflation, which can influence individuals’ economic behaviour (e.g. spending and saving decisions). The main insight from the review is that while consumers may have a limited ability to store and recall specific prices, and even succumb to a number of biases in the way in which they form perceptions and expectations of global price changes, they do seem to have some feel for, and ability to judge and forecast, inflation. How they achieve this, however, is still an open question, although plausible explanations have been proposed. While much important research has been undertaken and significant progress made in our understanding of the psychology of inflation, there remain many unanswered questions and interesting avenues for future research, which are discussed in the final part of the paper.     

Basic notions for the analysis of large two-mode networks

Many large real-world networks actually have a two-mode nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many ad hoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse large one-mode networks (the classical case) to the two-mode case. To achieve this, we introduce a set of simple statistics, which we discuss by comparing their values on a representative set of real-world networks and on their random versions. This makes it possible to evaluate their relevance in capturing properties of interest in two-mode networks.