The results of our study provide some degree of reassurance about the safety of HMG-CoA reductase inhibitors.

In pharmacoeconomics, the comparison of the costs of 2 different drugs used for the same treatment is of great interest. The problem is especially challenging when the drugs are likely to produce costly adverse effects in a small number of patients, which is often the case. The data are then skewed and traditional statistical methods to analyse the difference in the mean costs produced by 2 treatments may be inappropriate. The bootstrap method is presented as an alternative approach. A pharmacoeconomic cost-analysis example is presented and used throughout this article.

Linear regression is ubiquitous in statistical analysis. It is well understood that conflicting sources of information may contaminate the inference when the classical normality of errors is assumed. The contamination caused by the light normal tails follows from an undesirable effect: the posterior concentrates in an area in between the different sources with a large enough scaling to incorporate them all. The theory of conflict resolution in Bayesian statistics (O'Hagan and Pericchi (2012)) recommends to address this problem by limiting the impact of outliers to obtain conclusions consistent with the bulk of the data. In this paper, we propose a model with super heavy-tailed errors to achieve this. We prove that it is wholly robust, meaning that the impact of outliers gradually vanishes as they move further and further away form the general trend. The super heavy-tailed density is similar to the normal outside of the tails, which gives rise to an efficient estimation procedure. In addition, estimates are easily computed. This is highlighted via a detailed user guide, where all steps are explained through a case study. The performance is shown using simulation. All required code is given.MSC 2010 subject classifications: Primary 62F35; secondary 62J05.

Study Objectives Research indicates that sleep efficiency below 80% substantially increases mortality risk in elderly persons. The aim of this study was to identify factors that would best predict poor sleep efficiency in the elderly, and to determine whether associations between these factors and sleep efficiency were similar for men and women and for younger and older elderly persons. Methods A total of 2468 individuals aged 65–96 years (40.7% men) participated. They were recruited via random generation of telephone numbers according to a geographic sampling strategy. The participants agreed to have health professionals visit their home and to answer structured interview questions. Sleep efficiency was calculated based on interview responses. Descriptive statistics and logistic regressions were conducted. Results The factors most strongly associated with sleep efficiency below 80% were pain, nocturia, sleep medication use, and awakening from bad dreams. Some factors varied by sex: women aged 75 years and older or who had an anxiety disorder were more likely to have sleep efficiency below 80%, whereas being single or having painful illness raised the likelihood for men only. Except for sex, all the factors that showed associations with sleep efficiency affected younger and older elderly persons similarly. Conclusions Poor sleep efficiency is prevalent among elderly persons. The results shed new light on factors associated with poor sleep efficiency, highlighting the presence of sex differences and that certain factors make no significant contribution, such as typically proscribed sleep hygiene behaviors, mood disorders, and illness in general.