Influence of a home-based exercise program on the urine pH in elderly female subjects: a secondary analysis of a randomized controlled trial

Background

A low urine pH is a characteristic metabolic feature of metabolic syndrome and type 2 diabetes. The purpose of the current study was to investigate the effects of a 12-week home-based bench step exercise on the urine pH status of elderly female subjects.

Methods

The current study is a secondary analysis of a randomized controlled trial (RCT) in which 59 postmenopausal female subjects were randomized to either the exercise group (n?=?29) or the control group (n?=?30). The subjects in the exercise group were instructed to perform home-based exercises using a bench step at the anaerobic threshold (AT), with a goal of performing ≥140 min/week at home for 12 weeks. The subjects in the control group were instructed to not change their normal lifestyle. Urine was collected after overnight fasting, and the urine pH was measured using a urinary test strip. The inter-group-differences at baseline and the pre-post changes within groups were assessed using the Mann-Whitney U test and Wilcoxon’s signed-rank test, respectively. Additionally, the difference in the post-intervention urine pH levels of the two groups, adjusted for the pre-intervention values (the estimated effect size) and the precision (95% confidence intervals) were investigated using an analysis of covariance.

Results

The pre-post comparison of the urine pH data using Wilcoxon’s signed-rank test showed a significant increase in the urine pH levels of the exercise group (p?<?0.05); there was no significant change in the urine pH levels of the control group. However, the estimated effect size (0.15) was small and the confidence interval straddled 0 (?0.25–0.55).

Conclusions

Based on the results of the current secondary analysis of an RCT, we could not clearly conclude that exercise has a beneficial effect on the urine pH. Further well-designed RCTs should be conducted to determine whether aerobic exercise is truly able to ameliorate urine acidification.

Trial registration

The study was retrospectively registered in the University Hospital Medical Information Network (UMIN) as “Effect of step exercise on aerobic fitness and progression of atherosclerosis in the elderly” under the registration number UMIN 000026743 (the date of registration: March 28, 2017).

     

Approximate confidence intervals for the weighted kappa coefficient of a binary diagnostic test subject to a case–control design

Case–control design to assess the accuracy of a binary diagnostic test (BDT) is very frequent in clinical practice. This design consists of applying the diagnostic test to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. The sensitivity of the diagnostic test is estimated from the case sample and the specificity is estimated from the control sample. Another parameter which is used to assess the performance of a BDT is the weighted kappa coefficient. The weighted kappa coefficient depends on the sensitivity and specificity of the diagnostic test, on the disease prevalence and on the weighting index. In this article, confidence intervals are studied for the weighted kappa coefficient subject to a case–control design and a method is proposed to calculate the sample sizes to estimate this parameter. The results obtained were applied to a real example.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.     

Flexible Tweedie regression models for continuous data

Tweedie regression models (TRMs) provide a flexible family of distributions to deal with non-negative right-skewed data and can handle continuous data with probability mass at zero. Estimation and inference of TRMs based on the maximum likelihood (ML) method are challenged by the presence of an infinity sum in the probability function and non-trivial restrictions on the power parameter space. In this paper, we propose two approaches for fitting TRMs, namely quasi-likelihood (QML) and pseudo-likelihood (PML). We discuss their asymptotic properties and perform simulation studies to compare our methods with the ML method. We show that the QML method provides asymptotically efficient estimation for regression parameters. Simulation studies showed that the QML and PML approaches present estimates, standard errors and coverage rates similar to the ML method. Furthermore, the second-moment assumptions required by the QML and PML methods enable us to extend the TRMs to the class of quasi-TRMs in Wedderburn's style. It allows to eliminate the non-trivial restriction on the power parameter space, and thus provides a flexible regression model to deal with continuous data. We provide an R implementation and illustrate the application of TRMs using three data sets.     

Getting to Where You Want to Go: Designing Information Systems and Using Your Data Through the Application of Analytics and Informatics

ABSTRACT

This article, based on the closing session from the 2017 NC Serials Conference, describes a systems perspective where data serve as both real-time and summative information and knowledge about how an organization is performing, what services and resources its users are using and interacting with, and areas for which the organization is doing well or needs improvement. The author posits that data are most useful when the context is informed by clear goals that provide a direction to where the organization is and wants to be. Applied examples are discussed for several environments, including an academic department, school of education, regional libraries, and public libraries.     

Estimation and diagnostic for skew-normal partially linear models

Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

A comparison of L-, LQ-, TL-moment and maximum likelihood high quantile estimates of the GPD and GEV distribution

In this article, L-moments, LQ-moments and TL-moments of the generalized Pareto and generalized extreme-value distributions are derived up to the fourth order. The first three L-, LQ- and TL-moments are used to obtain estimators of their parameters. Performing a simulation study, high-quantile estimates based on L-, LQ-, and TL-moments are compared to the maximum likelihood estimate with respect to their sample mean squared error. This consists of identifying an optimal combination of parameters α and p both considered in the range [0, 0.5] for estimating quantiles by LQ-moments. The results show L-moment and maximum likelihood methods outperform other methods.     

Copulas related to piecewise monotone functions of the interval and associated processes

In this work, we derive the copulas related to vectors obtained from the so-called chaotic stochastic processes. These are defined by the iteration of certain piecewise monotone functions of the interval [0, 1] to some initial random variable. We study some of its properties and present some examples. Since often these types of copulas do not have closed formulas, we provide a general approximation method which converges uniformly to the true copula. Our results cover a wide class of processes, including the so-called Manneville–Pomeau processes. The general theory is applied to the parametric estimation in certain chaotic processes. A Monte Carlo simulation study is also presented.