Disability transitions in Dutch community-dwelling older people aged 75 years or older

BACKGROUND

Recent world population predictions show that the world population aged >=65 years will increase from 10% in 2022 to 16% in 2050. Population aging is accompanied by an increase in people with disability. It is important to pay special attention to people with disability, as these people are at high risk of adverse outcomes. Our study aimed to investigate the transitions of disability among Dutch community-dwelling older people aged 75 years or older, using a follow-up of nine years. We used socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events to predict the disability transitions over time.

METHODS

We used a sample of 484 people that was randomly drawn from the municipality of Roosendaal (the Netherlands), a municipality with 78,000 inhabitants. A subset of people who completed part A of the Tilburg Frailty Indicator (TFI) at baseline and the Groningen Activity Restriction Scale (GARS) questionnaires was used with a nine-year follow-up. Paired Wilcoxon tests were used to compare the consecutive measurements. Socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events were included to predict the disability transitions over time. For the univariable and multivariable analysis of the measurements over time with the predictor variables, we used generalized estimation equations (GEE). A p-value <0.05 was considered significant. R version 3.4.4 was used for all analyses.

RESULTS

Of the participants, 65% were younger than 80 years, 50% were married or cohabiting, 87% reported a healthy lifestyle, and 63% had no diseases or chronic disorders. Each year, more participants changed from status not disabled to disabled than vice versa. The GEE analyses showed that lifestyle ('not healthy') and diseases or chronic disorders ('two or more') were significant in the multivariable analysis for the disability score and only diseases or chronic disorders ('two or more') was significant in the multivariable analysis for the dichotomous disability score.

CONCLUSIONS

The transition of the disability score is strongly influenced by lifestyle and diseases or disorders. This applies to a lesser extent to the dichotomous disability score. There, only diseases or disorders are an important predictor. For health care professionals our study provides starting points for interventions focused on the prevention of worsening disability and for community-dwelling older people >= 75, the most important recommendation is: live healthy!

Should We Continue EU Cohesion Policy? The Dilemma of Uneven Development of Polish Regions

The European Union and the whole world are facing the problem of increasing income and wealth inequalities at the country and regional levels. Inequalities are a severe obstacle to sustainable and balanced growth. Politicians perceive this problem by trying to implement different types of development models. The cohesion policy is the European Union's response to the development differences between countries and regions. It is clearly defined that development has to be sustainable and inclusive, which is reflected in the objectives of all leading EU development strategies. The aim of this paper is (1) to examine how effective the EU cohesion policy is in reducing regional disparities (2) to assess that the main cause of regional disparities is the misallocation of human capital. The authors base their study on the regions of Poland, estimating the state of their human capital and innovation between 2004 and 2018. We examine the effectiveness of cohesion policy using functional data analysis-the Wilcoxon test, which was used for the first time for this type of issue. The current question is whether to help metropolises and large cities, which will entail the development of smaller units, or whether to try to increase the rate of development of smaller and economically weaker territorial units in the first place.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11205-022-03048-8.

Progressive improvement of DRASTICA and SI models for groundwater vulnerability assessment based on evolutionary algorithms

Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO₃, SO₄, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO₄ concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO₃ concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO₄ and NO₃ concentrations, the DRASTICA and SI models with modified rates and optimized weights exhibited enhanced correlation with toxic metals of about 0.7 and 0.2, respectively.

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance

A nonparametric test labelled ‘Rao Spacing-frequencies test’ is explored and developed for testing whether two circular samples come from the same population. Its exact distribution and performance relative to comparable tests such as the Wheeler-Watson test and the Dixon test in small samples, are discussed. Although this test statistic is shown to be asymptotically normal, as one would expect, this large sample distribution does not provide satisfactory approximations for small to moderate samples. Exact critical values for small samples are obtained and tables provided here, using combinatorial techniques, and asymptotic critical regions are assessed against these. For moderate sample sizes in-between i.e. when the samples are too large making combinatorial techniques computationally prohibitive but yet asymptotic regions do not provide a good approximation, we provide a simple Monte Carlo procedure that gives very accurate critical values. As is well-known, the large number of usual rank-based tests are not applicable in the context of circular data since the values of such ranks depend on the arbitrary choice of origin and the sense of rotation used (clockwise or anti-clockwise). Tests that are invariant under the group of rotations, depend on the data through the so-called ‘spacing frequencies’, the frequencies of one sample that fall in between the spacings (or gaps) made by the other. The Wheeler-Watson, Dixon, and the proposed Rao tests are of this form and are explicitly useful for circular data, but they also have the added advantage of being valid and useful for comparing any two samples on the real line. Our study and simulations establish the ‘Rao spacing-frequencies test’ as a desirable, and indeed preferable test in a wide variety of contexts for comparing two circular samples, and as a viable competitor even for data on the real line. Computational help for implementing any of these tests, is made available online “TwoCircles” R package and is part of this paper.

Forecasts of tropospheric ozone in the Metropolitan Area of Rio de Janeiro based on missing data imputation and multivariate calibration techniques

Multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018. These techniques provide an easy and feasible way of modeling and analyzing air pollutants and can be used when coupled with other methods. The results showed that random forest and support vector machine chemometric techniques can be used in modeling and predicting tropospheric ozone concentrations, with a coefficient of determination for making predictions up to 0.92, a root-mean square error of calibration between 4.66 and 27.15 µg m^-3, and a root-mean square error of prediction between 4.17 and 22.45 µg m^-3, depending on the air quality monitoring stations and season.

Groundwater vulnerability mapping using the modified DRASTIC model: the metaheuristic algorithm approach

Vulnerability assessment and mapping is a significant tool for sustainable management of the precious natural groundwater resources. DRASTIC is an extensively used index model to map groundwater vulnerable zones. However, the original DRASTIC model rates and weights used in most of the research depict the poor correlation between nitrate concentration and groundwater vulnerability index. Wilcoxon test and five population-based metaheuristic (MH) algorithms, namely, firefly algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), shuffled frog leaping algorithm (SFLA), and particle swarm optimization (PSO), were used to optimize the rates and weights of the DRASTIC model to improve its accuracy. The performance of all the employed metaheuristic algorithms converges to a global optimal solution at different iterations, and to choose the best algorithm for DRASTIC weights optimization, a ranking methodology was proposed. The algorithms were ranked by calculating the relative closeness of alternatives with computational speed and the number of iterations as attributes in the TOPSIS method. This study identifies FA as the outperforming algorithm among the employed for this specified weight optimization problem based on ranking. The result of the optimization model proposed depicts significant improvement in the correlation coefficient between the groundwater vulnerability index and nitrate concentration from 0.0545 for the original DRASTIC model to 0.7247 for the Wilcoxon-MH- DRASTIC. Hence, this ranking approach can be adopted when global optimal solution is found by all employed algorithms in DRASTIC weight optimization.

Adjusted win ratio with stratification: Calculation methods and interpretation

The win ratio is a general method of comparing locations of distributions of two independent, ordinal random variables, and it can be estimated without distributional assumptions. In this paper we provide a unified theory of win ratio estimation in the presence of stratification and adjustment by a numeric variable. Building step by step on the estimate of the crude win ratio we compare corresponding tests with well known non-parametric tests of group difference (Wilcoxon rank-sum test, Fligner-Policello test, van Elteren test, test based on the regression on ranks, and the rank analysis of covariance test). We show that the win ratio gives an interpretable treatment effect measure with corresponding test to detect treatment effect difference under minimal assumptions.

A resampling-based test for two crossing survival curves

The area between two survival curves is an intuitive test statistic for the classical two-sample testing problem. We propose a bootstrap version of it for assessing the overall homogeneity of these curves. Our approach allows ties in the data as well as independent right censoring, which may differ between the groups. The asymptotic distribution of the test statistic as well as of its bootstrap counterpart are derived under the null hypothesis, and their consistency is proven for general alternatives. We demonstrate the finite sample superiority of the proposed test over some existing methods in a simulation study and illustrate its application by a real-data example.

Statistical methods for identification of golden ratio

Several biological systems such as the biomechanics of human heart, locomotion, and phyllotaxis of plants present a harmonic behavior because their fractal structure are associated to the golden ratio. The golden ratio (Φ = 1.618033988749…), also known as Phi, golden mean, golden section or divine proportion, is an irrational constant found in various forms in nature and recently has been used in many health areas. However, there is no literature on a specific statistical test to identify the golden ratio structures. To validate the results from each survey, it is necessary that statistical techniques be correctly selected and implemented, and the absence of a test to identify the golden ratio may undermines the scientific papers which have this goal. Since the golden number is a ratio, some tests have been wrongly applied in its identification. The objective of this paper is to present and to evaluate methods for identification of golden ratio. Four tests were evaluated: t-Student with ratio statistic (TR), with delta statistic (TΔ), with difference statistic (TED), and Wilcoxon test with statistic difference (WD). Data simulating different samples sizes (n = 2-200) and variability scenarios were used. The tests were assessed regarding type I error rate and power. For TΔ, type I error rate increased along with sample size and variability, achieving 50% in the scenario of relative standard deviation of 12.5% and 20.0% for line segments of lengths a and b, and sample size equal 200. This test also showed lower power when compared to the others in all scenarios. Similarly, for TR, the type I error rate was sensitive to the increasing in sample size, varying from 5 to 60%. On the other hand, WD and TED were associated to low type I error rates (around 5%) and high power (6.1% for sample size equal 2-100% for sample size equal 200). The TΔ and TR were inadequate to identify the golden ratio, since they did not controlled the type I error rate and/or presented low power, leading to possible erroneous conclusions. Therefore WD and TED, both with statistical of difference, appeared as the most appropriate methods to test golden ratio structures.

Proposal of a new method to prove that unnecessary information is not drawn on the image using statistical analysis

The purpose of this study is to propose a new method of image evaluation using statistical analysis. We used the Sign test and the Wilcoxon test to analyze the statistical significance of image differences. Using this method, we evaluated whether the small electrode of the DAP meter appears in the X-ray image. Two observed values, which were obtained by averaging all values under all exposure conditions, were compared. All the observation tests showed the same sign. Thus, the results proved that the small electrode of the DAP meter is not present on the image. Using this method, it became possible to prove that the electrode was not depicted, which was impossible to determine using conventional methods. The method combining both the Sign test and the Wilcoxon test can be useful in image evaluation.

To t-Test or Not to t-Test? A p-Values-Based Point of View in the Receiver Operating Characteristic Curve Framework

A common statistical doctrine supported by many introductory courses and textbooks is that t-test type procedures based on normally distributed data points are anticipated to provide a standard in decision-making. In order to motivate scholars to examine this convention, we introduce a simple approach based on graphical tools of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. In this context, we propose employing a p-values-based method, taking into account the stochastic nature of p-values. We focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we extend the EPV concept to be considered in terms of the ROC curve technique. This provides expressive evaluations and visualizations of a wide spectrum of testing mechanisms' properties. We show that the conventional power characterization of tests is a partial aspect of the presented EPV/ROC technique. We desire that this explanation of the EPV/ROC approach convinces researchers of the usefulness of the EPV/ROC approach for depicting different characteristics of decision-making procedures, in light of the growing interest regarding correct p-values-based applications.

Comparison of Saliva Nitric Oxide between Chronic Kidney Disease Before and After Dialysis and with Control Group

Introduction:

Chronic Kidney Disease (CKD) is a chronic progressive disorder and a major cause of death and disability in all countries. In the kidneys, Nitric Oxide (NO) has involved in several important cellular processes including glomerular and modular hemodynamics set-out, tubular - glomerular feedback reaction, renin releasing and extracellular fluid volume but NO can act as an inflammatory mediator and oxidative stress factor in high levels.

Aim:

The aim of this study was to evaluate salivary levels of NO in patients with chronic kidney disease on dialysis compared to the healthy subjects and evaluate the effect of dialysis on the level of NO in saliva.

Materials & Methods:

In this case-control study, 30 hemodialysis patients and 30 healthy controls that were matched for age and sex were selected. Unstimulated saliva samples were collected from all subjects. In the patient’s group, half an hour before starting dialysis first sampling and two hours after the completion of dialysis second sampling were collected. NO concentration in the samples was measured by using the Griess method. For data analysis, SPSS software version 16, Mann Whitney-U and Wilcoxon test were used. The level of significance was considered 0.05.

Results:

Mann-Whitney U test showed that the average concentration of salivary NO in patients with CKD (pre-dialysis and after dialysis) was higher than in the control group. The average concentration of salivary NO in patients with CKD was reduced after hemodialysis.

Conclusion:

Hemodialysis reduces salivary NO levels in CKD patients. It seems that hemodialysis has a role in decreasing the concentration of this inflammatory mediator and oxidative stress.

Non-parametric Survival Analysis of EPG5 Gene with Age at Onset of Alzheimer's Disease

Non-parametric methods such as Wilcoxon test have the advantages of no assumptions for the underlying survival distributions. Alzheimer's disease (AD) is a chronic neurodegenerative disease while the ectopic P-granules autophagy protein 5 homolog (EPG5 gene) is highly expressed in human brain and may implicate in the pathogenesis of neurodegenerative disorders. The present study explored the associations of 26 single-nucleotide polymorphisms (SNPs) in the EPG5 gene with the age at onset (AAO) of AD using a family-based association test (FBAT)-Wilcoxon statistic in a family-based study. Then a replication study using a case-control sample was conducted to perform Wilcoxon test in Kaplan-Meier survival analysis of AAO. The results from FBAT-generalized estimating equations (FBAT-GEE) statistics and FBAT-Wilcoxon test showed that seven SNPs (top SNP rs495078 with p = 1.29 × 10-3) were significantly associated with the risk of AD, and eight SNPs (top SNP rs11082498 with p = 3.55 × 10-4) were associated with the AAO of AD in the family-based study (p < 0.05). In the replicated data, three SNPs were associated with AAO by using the Wilcoxon test, where the mean AAO was approximately 2.2 years earlier in individuals who had at least one minor allele of the top AAO-associated SNP rs9963463 (p = 0.0018) compared with those who were homozygous for the major allele. These findings from non-parametric survival analyses provide evidence for several genetic variants in EPG5 influencing the AAO of AD and will serve as a resource for replication in other populations.

How to analyze the Visual Analogue Scale: Myths, truths and clinical relevance

BACKGROUND AND AIMS

The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores.

METHODS

We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations.

RESULTS

We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions.

CONCLUSIONS AND IMPLICATIONS

We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann-Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods (t-test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods.

Strange Data: When the Numbers Just Aren't Normal

Many statistical tests assume that the populations from which we draw our data samples roughly follow a given probability distribution. Here, I review what these assumptions mean, why they are important, and how to deal with situations where the assumptions are not met.

Converging or Crossing Curves: Untie the Gordian Knot or Cut it? Appropriate Statistics for Non-Proportional Hazards in Decitabine DACO-016 Study (AML)

Introduction

Among patients with acute myeloid leukemia (AML), the DACO-016 randomized study showed reduction in mortality for decitabine [Dacogen^® (DAC), Eisai Inc., Woodcliff Lake, NJ, USA] compared with treatment choice (TC): at primary analysis the hazard ratio (HR) was 0.85 (95% confidence interval 0.69–1.04; stratified log-rank P = 0.108). With two interim analyses, two-sided alpha was adjusted to 0.0462. With 1-year additional follow-up the HR reached 0.82 (nominal P = 0.0373). These data resulted in approval of DAC in the European Union, though not in the United States. Though pre-specified, the log-rank test could be considered not optimal to assess the observed survival difference because of the non-proportional hazard nature of the survival curves.

Methods

We applied the Wilcoxon test as a sensitivity analysis. Patients were randomized to DAC (N = 242) or TC (N = 243). One-hundred and eight (44.4%) patients in the TC arm and 91 (37.6%) patients in the DAC arm selectively crossed over to subsequent disease modifying therapies at progression, which might impact the survival beyond the median with resultant converging curves (and disproportional hazards).

Results

The stratified Wilcoxon test showed a significant improvement in median (CI 95%) overall survival with DAC [7.7 (6.2; 9.2) months] versus TC [5.0 (4.3; 6.3) months; P = 0.0458].

Conclusion

Wilcoxon test indicated significant increase in survival for DAC versus TC compared to log-rank test.

Funding

Janssen-Cilag GmbH.

Electronic supplementary material

The online version of this article (doi:10.1007/s12325-015-0238-9) contains supplementary material, which is available to authorized users.

Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R package

For right-censored data perhaps the most commonly used tests are weighted logrank tests, such as the logrank and Wilcoxon-type tests. In this paper we review several generalizations of those weighted logrank tests to interval-censored data and present an R package, interval, to implement many of them. The interval package depends on the perm package, also presented here, which performs exact and asymptotic linear permutation tests. The perm package performs many of the tests included in the already available coin package, and provides an independent validation of coin. We review analysis methods for interval-censored data, and we describe and show how to use the interval and perm packages.