Cognitive function is associated with risk aversion in community-based older persons

<p>Abstract</p> <p>Background</p> <p>Emerging data from younger and middle-aged persons suggest that cognitive ability is negatively associated with risk aversion, but this association has not been studied among older persons who are at high risk of experiencing loss of cognitive function.</p> <p>Methods</p> <p>Using data from 369 community-dwelling older persons without dementia from the Rush Memory and Aging Project, an ongoing longitudinal epidemiologic study of aging, we examined the correlates of risk aversion and tested the hypothesis that cognition is negatively associated with risk aversion. Global cognition and five specific cognitive abilities were measured via detailed cognitive testing, and risk aversion was measured using standard behavioral economics questions in which participants were asked to choose between a certain monetary payment ($15) versus a gamble in which they could gain more than$15 or gain nothing; potential gamble gains ranged from $21.79 to$151.19 with the gain amounts varied randomly over questions. We first examined the bivariate associations of age, education, sex, income and cognition with risk aversion. Next, we examined the associations between cognition and risk aversion via mixed models adjusted for age, sex, education, and income. Finally, we conducted sensitivity analyses to ensure that our results were not driven by persons with preclinical cognitive impairment.</p> <p>Results</p> <p>In bivariate analyses, sex, education, income and global cognition were associated with risk aversion. However, in a mixed effect model, only sex (estimate = -1.49, standard error (SE) = 0.39, p < 0.001) and global cognitive function (estimate = -1.05, standard error (SE) = 0.34, p < 0.003) were significantly inversely associated with risk aversion. Thus, a lower level of global cognitive function and female sex were associated with greater risk aversion. Moreover, performance on four out of the five cognitive domains was negatively related to risk aversion (<it>i.e</it>., semantic memory, episodic memory, working memory, and perceptual speed); performance on visuospatial abilities was not.</p> <p>Conclusion</p> <p>A lower level of cognitive ability and female sex are associated with greater risk aversion in advanced age.</p

Regression analysis for discrete event history or failure time data

The paper deals with discrete-time regression models to analyze multistate—multiepisode models for event history data or failure time data collected in follow-up studies, retrospective studies, or longitudinal panels. The models are applicable if the events are not dated exactly but only a time interval is recorded. The models include individual specific parameters to account for unobserved heterogeneity. The explantory variables may be time-varying and random with distributions depending on the observed history of the process. Different estimation procedures are considered: Estimation of structural as well as individual specific parameters by maximization of a joint likelihood function, estimation of the structural parameters by maximization of a conditional likelihood function conditioning on a set of sufficient statistics for the individual specific parameters, and estimation of the structural parameters by maximization of a marginal likelihood function assuming that the individual specific parameters follow a distribution. The advantages and limitations of the different approaches are discussed

Analysis of familial aggregation in the presence of varying family sizes

Family studies are frequently undertaken as the first step in the search for genetic and/or environmental determinants of disease. Significant familial aggregation of disease is suggestive of a genetic aetiology for the disease and may lead to more focused genetic analysis. Of course, it may also be due to shared environmental factors. Many methods have been proposed in the literature for the analysis of family studies. One model that is appealing for the simplicity of its computation and the conditional interpretation of its parameters is the quadratic exponential model. However, a limiting factor in its application is that it is not "reproducible", meaning that all families must be of the same size. To increase the applicability of this model, we propose a hybrid approach in which analysis is based on the assumption of the quadratic exponential model for a selected family size and combines a "missing data" approach for smaller families with a "marginalization" approach for larger families. We apply our approach to a family study of colorectal cancer that was sponsored by the Cancer Genetics Network of the National Institutes of Health. We investigate the properties of our approach in simulation studies. Our approach applies more generally to clustered binary data. Copyright 2005 Royal Statistical Society.

Performance of Three Estimation Methods in Repeated Time-to-Event Modeling

It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation–maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance