A joint model of cancer incidence,metastasis, and mortality

Many diseases, especially cancer, are not static, but rather can be summarized by a series of events or stages (e.g. diagnosis, remission, recurrence, metastasis, death). Most available methods to analyze multi-stage data ignore intermediate events and focus on the terminal event or consider (time to) multiple events as independent. Competing-risk or semi-competing-risk models are often deficient in describing the complex relationship between disease progression events which are driven by a shared progression stochastic process. A multi-stage model can only examine two stages at a time and thus fails to capture the effect of one stage on the time spent between other stages. Moreover, most models do not account for latent stages. We propose a semi-parametric joint model of diagnosis, latent metastasis, and cancer death and use nonparametric maximum likelihood to estimate covariate effects on the risks of intermediate events and death and the dependence between them. We illustrate the model with Monte Carlo simulations and analysis of real data on prostate cancer from the SEER database.     

A distance-based model for spatial prediction using radial basis functions

In the context of local interpolators, radial basis functions (RBFs) are known to reduce the computational time by using a subset of the data for prediction purposes. In this paper, we propose a new distance-based spatial RBFs method which allows modeling spatial continuous random variables. The trend is incorporated into a RBF according to a detrending procedure with mixed variables, among which we may have categorical variables. In order to evaluate the efficiency of the proposed method, a simulation study is carried out for a variety of practical scenarios for five distinct RBFs, incorporating principal coordinates. Finally, the proposed method is illustrated with an application of prediction of calcium concentration measured at a depth of 0–20 cm in Brazil, selecting the smoothing parameter by cross-validation.     

More nonparametric Bayesian inference in applications

Discussion of “Nonparametric Bayesian Inference in Applications” by Peter Mueller, Fernando A. Quintana, Garritt Page: More Nonparametric Bayesian Inference in Applications.     

Risk attitudes in a social context

Many experiments have demonstrated that when evaluating payoffs, people take not only their own payoffs into account, but also the payoffs of others in their social environment. Most of this evidence is found in settings where payoffs are riskless. It is plausible that if people care about the payoffs of others, they do so not only in a riskless context, but also in a risky one. This suggests that an individual’s decision making under risk depends on the risks others in his or her environment face. This paper is the first to test whether individuals’ risk attitudes are affected by the risks others face. The results show that risk attitudes appear to be less affected by others’ risks than expected, even though the same subjects do show concerns for inequality in a riskless setting. Interestingly, we find that people prefer risks to be independent across individuals in society rather than correlated.     

Dopamine and risk choices in different domains: Findings among serious tournament bridge players

We explore how risk-taking in the card game contract bridge, and in a financial gamble, correlate with variation in the dopamine receptor D4 gene (DRD4) among serious tournament bridge players. In bridge risk-taking, we find significant interactions between genetic predisposition and skill. Among men with the 7-repeat allele of DRD4, namely 7R + men, those with more bridge skill take more good risks and fewer bad risks, while the opposite is found for less-expert 7R + men. Conversely, skill does not predict risk-taking among men without the 7R + allele. Consistent with some prior studies, we also find that 7R + men take more risk in the financial gamble. We find no relationship between 7R + and either risk measure among our female subjects. Our results suggest that the dopamine system plays an important role in individual differences in risk-taking among men, and is the first to distinguish between advantageous and disadvantageous risk-taking.     

A decision-theoretical view of default priors

In this article, we outline a simple and intuitively appealing procedure to derive default priors. The main idea is to regard the choice of such a prior as a formal Bayesian decision problem. We also discuss Jeffreys prior and more generally the reference prior of Bernardo (J R Stat Soc B 41:113–147, 1979) from this standpoint.     

‘Drink a 12 box before you go’: pre-loading among young people in Aotearoa New Zealand

The practice of pre-loading—drinking large amounts of alcohol rapidly in private spaces prior to socialising in the night-time economy—has come to notice recently in the study of alcohol-related harm, but no studies have explored these phenomena in Aotearoa New Zealand. We used a theoretical framework developed with public health alcohol studies for understanding drinking cultures that conceptualises patterns of behaviours as arising within a dynamic interaction between forces of hedonism, function and control. We report findings from 34 focus groups conducted with 18–25 year olds as part of a project supported by the Marsden Fund, between 2011 and 2012, to investigate drinking cultures among young people. Our thematic analyses of participants’ accounts of pre-loading show that the term is in common use, applying to a range of practices motivated by price of alcohol but influenced by the pleasures of intoxication, the importance of peer processes and certain aspects of the regulatory system. We conclude with a discussion of the usefulness of the framework and the implications of the findings for public health policy that aims to reduce alcohol consumption and the harm that arises from it.     

Accuracy and bias in self-perception of performance: Narcissism matters in Korea as well

Debates regarding the nature of self-enhancement versus accurate self-assessment have been active among psychologists for decades. More recently, researchers have become interested in the panculturality of self-enhancement. Some researchers argue that self-enhancement is universal and present within all cultures. Others declare self-enhancement to be a Western tendency, with self-diminishment being the norm among East Asians. Importantly, the majority of such studies have not compared self-perceptions against objective external criteria, especially those with East Asians. Furthermore, the link between narcissism and self-enhancement has been largely overlooked within Korean samples. To address such gaps, we utilized scores on an objective test as a criterion to investigate the accuracy of Koreans’ self-assessments of performance, as well as how individual differences in narcissism are related to such assessments. A sample of Korean students (N = 146; 71 women) completed self-report measures of narcissism and self-esteem, and took a listening comprehension quiz. Estimated and actual scores were collected and used to compute self-enhancement scores. Results demonstrated that Koreans’ self-perceptions of performance on the quiz were quite accurate. As has been found in Western cultures, narcissism was related to self-enhancement.     

Spectral estimation for locally stationary time series with missing observations

Time series arising in practice often have an inherently irregular sampling structure or missing values, that can arise for example due to a faulty measuring device or complex time-dependent nature. Spectral decomposition of time series is a traditionally useful tool for data variability analysis. However, existing methods for spectral estimation often assume a regularly-sampled time series, or require modifications to cope with irregular or ‘gappy’ data. Additionally, many techniques also assume that the time series are stationary, which in the majority of cases is demonstrably not appropriate. This article addresses the topic of spectral estimation of a non-stationary time series sampled with missing data. The time series is modelled as a locally stationary wavelet process in the sense introduced by Nason et al. (J. R. Stat. Soc. B 62(2):271–292, 2000) and its realization is assumed to feature missing observations. Our work proposes an estimator (the periodogram) for the process wavelet spectrum, which copes with the missing data whilst relaxing the strong assumption of stationarity. At the centre of our construction are second generation wavelets built by means of the lifting scheme (Sweldens, Wavelet Applications in Signal and Image Processing III, Proc. SPIE, vol. 2569, pp. 68–79, 1995), designed to cope with irregular data. We investigate the theoretical properties of our proposed periodogram, and show that it can be smoothed to produce a bias-corrected spectral estimate by adopting a penalized least squares criterion. We demonstrate our method with real data and simulated examples.