Changes in executive function after acute bouts of passive cycling in Parkinson's disease

Individuals with Parkinson's disease (PD) often experience cognitive declines. Although pharmacologic therapies are helpful in treating motor deficits in PD, they do not appear to be effective for cognitive complications. Acute bouts of moderate aerobic exercise have been shown to improve cognitive function in healthy adults. However, individuals with PD often have difficulty with exercise. This study examined the effects of passive leg cycling on executive function in PD. Executive function was assessed with Trail-Making Test (TMT) A and B before and after passive leg cycling. Significant improvements on the TMT-B test occurred after passive leg cycling. Furthermore, the difference between times to complete the TMT-B and TMT-A significantly decreased from precycling to postcycling. Improved executive function after passive cycling may be a result of increases in cerebral blood flow. These findings suggest that passive exercise could be a concurrent therapy for cognitive decline in PD.     

Product Positioning Using a Self‐Organizing Map and the Rings of Influence

In this article, we propose a new product positioning method based on the neural network methodology of a self‐organizing map. The method incorporates the concept of rings of influence, where a firm evaluates individual consumers and decides on the intensity to pursue a consumer, based on the probability that this consumer will purchase a competing product. The method has several advantages over earlier work. First, no limitations are imposed on the number of competing products and second, the method can position multiple products in multiple market segments. Using simulations, we compare the new product positioning method with a quasi‐Newton method and find that the new method always approaches the best solution obtained by the quasi‐Newton method. The quasi‐Newton method, however, is dependent on the initial positions of the new products, with the majority of cases ending in a local optimum. Furthermore, the computational time required by the quasi‐Newton method increases exponentially, while the time required by the new method is small and remains almost unchanged, when the number of new products positioned increases. We also compute the expected utility that a firm will provide consumers by offering its products. We show that as the intensity with which a firm pursues consumers increases, the new method results in near‐optimal solutions in terms of market share, but with higher expected utility provided to consumers when compared to that obtained by a quasi‐Newton method. Thus, the new method can serve as a managerial decision‐making tool to compare the short‐term market share objective with the long‐term expected utility that a firm will provide to consumers, when it positions its products and intensifies its effort to attract consumers away from competition.     

Existence of unbiased covariance components estimators

The condition of PINCUS (1974) for the estimability of covariance components in normal models is extended to the case of singular covariance matrices     

Combining an Internal Pilot with an Interim Analysis for Single Degree of Freedom Tests

An internal pilot with interim analysis (IPIA) design combines interim power analysis (an internal pilot) with interim data analysis (two stage group sequential). We provide IPIA methods for single df hypotheses within the Gaussian general linear model, including one and two group t tests. The design allows early stopping for efficacy and futility while also re-estimating sample size based on an interim variance estimate. Study planning in small samples requires the exact and computable forms reported here. The formulation gives fast and accurate calculations of power, type I error rate, and expected sample size.     

Fast Computation by Block Permanents of Cumulative Distribution Functions of Order Statistics from Several Populations

The joint cumulative distribution function for order statistics arising from several different populations is given in terms of the distribution functions of the populations. The computational cost of our formula in the case of two populations is still exponential in the worst case, but it is a dramatic improvement compared to the general formula by Bapat and Beg. In the case when only the joint distribution function of a subset of the order statistics of fixed size is needed, the complexity is polynomial, for the case of two populations.     

Expected Power for the False Discovery Rate with Independence

The Benjamini–Hochberg procedure is widely used in multiple comparisons. Previous power results for this procedure have been based on simulations. This article produces theoretical expressions for expected power. To derive them, we make assumptions about the number of hypotheses being tested, which null hypotheses are true, which are false, and the distributions of the test statistics under each null and alternative. We use these assumptions to derive bounds for multiple dimensional rejection regions. With these bounds and a permanent based representation of the joint density function of the largest p-values, we use the law of total probability to derive the distribution of the total number of rejections. We derive the joint distribution of the total number of rejections and the number of rejections when the null hypothesis is true. We give an analytic expression for the expected power for a false discovery rate procedure that assumes the hypotheses are independent.     

Bias in linear model power and sample size calculation due to estimating noncentrality

Data analysts frequently calculate power and sample size for a planned study using mean and variance estimates from an initial trial. Hence power,or the sample size needed to achieve a fixed power, varies randomly. Such claculations can be very inaccurate in the General Linear Univeriate Model (GLUM). Biased noncentrality estimators and censored power calculations create inaccuracy. Censoring occurs if only certain outcomes of an initial trial lead to a power calculation. For example, a confirmatory study may be planned (and a sample size estimated) only following a significant resulte in the initial trial.

Computing accurate point estimates or confidence bounds of GLUM noncentrality, power, or sample size in the presence of censoring involves truncated noncentral F distributions. We recommed confidence bounds, whether or not censoring occurs. A power analysis of data from humans exposed to carbon monoxide demonstrates the substantial impact on samle size that may occur. The results highlight potential; biases and should aid study planning and interpretation.     

Participation in Voluntary Youth‐Serving Associations and Early Adult Voting Behavior*

Objective. We investigate adolescent membership in voluntary associations and whether participation in these activities influences voting behavior during early adulthood. Methods. Weighted logistic regression models predicting membership in voluntary associations and voting behavior were estimated using data from the National Educational Longitudinal Study of 1988–1994 (NELS). Results. Our findings suggest that membership in voluntary associations varies by race and socioeconomic status (SES). In addition, membership in organizations historically rooted in moral development and civic socialization positively predict voter‐registration status and whether or not young adults participated in the first national election that they were eligible to vote in, but this relationship is moderated by both race and SES. Conclusions. Our findings suggest that a large proportion of U.S. teenagers still participate in community‐based programs, many of which foster later civic participation, but that all youth do not equally benefit from participation.     

Properties of doubly-truncated gamma variables

The truncated gamma distribution has been widely studied, primarily in life-testing and reliability settings. Most work has assumed an upper bound on the support of the random variable, i.e. the space of the distribution is (0,u). We consider a doubly-truncated gamma random variable restricted by both a lower (l) and upper (u) truncation point, both of which are considered known. We provide simple forms for the density, cumulative distribution function (CDF), moment generating function, cumulant generating function, characteristic function, and moments. We extend the results to describe the density, CDF, and moments of a doubly-truncated noncentral chi-square variable.