Conventional Statistical Tests Research Articles

A lifestyle physical activity intervention for women in alcohol treatment: A pilot randomized controlled trial

IntroductionCompared to men, women with alcohol use disorder (AUD) are more likely to drink to manage stress and negative affect. Given women's risk for poor drinking outcomes, it is critical to develop and test interventions that target these affective factors. Physical activity improves negative affect and has emerged as a promising adjunct to AUD treatment and, thus, may be especially valuable for women. MethodsFifty women with AUD (49.9 ± 12.0 years of age) participated in either a 12-week telephone-delivered lifestyle physical activity plus Fitbit (LPA + Fitbit) or a health education contact (HEC) control intervention following a partial hospital addictions treatment program. The study examined changes in drinking behaviors, mental health outcomes, and physical activity engagement post-intervention using both conventional test statistics and standard effect sizes. ResultsHigher rates of continuous abstinence during the 12-week period were observed in the LPA + Fitbit condition (55.6 %) than in the HEC condition (33.6 %); odds ratio = 2.97. However, among women who drank any alcohol during the 12-weeks, slightly higher rates of heavy drinking and drinks/day were observed among women in the LPA + Fitbit condition. Significant differences for improved mental health outcomes (including depression, anxiety, negative affect, positive affect, perceived stress, and behavioral activation) and increased self-reported physical activity were consistently observed among participants in the LPA + Fitbit condition, relative to HEC. ConclusionsThe LPA + Fitbit program had a positive impact on alcohol abstinence, mental health, and physical activity in adult women receiving treatment for AUD. Future research should continue to investigate the optimal implementation strategies, duration, and intensity of LPA interventions in the context of a fully-powered RCT.

Gut microbiota composition in travellers is associated with faecal lipocalin-2, a mediator of gut inflammation.

We examined the gut microbiota of travellers returning from tropical areas with and without traveller's diarrhoea (TD) and its association with faecal lipocalin-2 (LCN2) levels. Participants were recruited at the Hospital Clinic of Barcelona, Spain, and a single stool sample was collected from each individual to perform the diagnostic of the etiological agent causing gastrointestinal symptoms as well as to measure levels of faecal LCN2 as a biomarker of gut inflammation. We also characterised the composition of the gut microbiota by sequencing the region V3-V4 from the 16S rRNA gene, and assessed its relation with the clinical presentation of TD and LCN2 levels using a combination of conventional statistical tests and unsupervised machine learning approaches. Among 61 participants, 45 had TD, with 40% having identifiable etiological agents. Surprisingly, LCN2 levels were similar across groups, suggesting gut inflammation occurs without clinical TD symptoms. Differential abundance (DA) testing highlighted a microbial profile tied to high LCN2 levels, marked by increased Proteobacteria and Escherichia-Shigella, and decreased Firmicutes, notably Oscillospiraceae. UMAP analysis confirmed this profile's association, revealing distinct clusters based on LCN2 levels. The study underscores the discriminatory power of UMAP in capturing meaningful microbial patterns related to clinical variables. No relevant differences in the gut microbiota composition were found between travellers with or without TD. The findings suggest a correlation between gut microbiome and LCN2 levels during travel, emphasising the need for further research to discern the nature of this relationship.

Efficacy and safety of Apixaban for the prevention of thrombosis in arteriovenous grafts.

This research aims to fill a vital gap in existing studies by evaluating the efficacy and safety of Apixaban, a direct oral anticoagulant, in the prevention of arteriovenous graft (AVG) thrombosis, thereby offering substantial insights into alternative anticoagulant options for hemodialysis patients. Conducted as a multi-center, randomized, double-blind, placebo-controlled trial, this study involved end-stage renal disease (ESRD) patients who had recently received polytetrafluoroethylene grafts. Participants were assigned to receive either Apixaban at a dose of 2.5 mg twice daily or a placebo. The primary outcome measure was the occurrence of graft thrombosis, while secondary outcomes focused on the incidence and severity of bleeding. Analytical methods included Kaplan-Meier estimates, Cox proportional hazards models, and conventional statistical tests. With 96 patients enrolled, the study found that Apixaban significantly reduced the incidence of AVG thrombosis compared to placebo (16.7% vs. 62.5%, P < 0.0001). Notably, this reduction in thrombosis incidence was not accompanied by an increase in bleeding events, thus affirming the safety profile of Apixaban as established in prior research. Apixaban is identified as an efficacious alternative to traditional anticoagulants in the prevention of AVG thrombosis among hemodialysis patients, representing a notable advancement in the care of individuals with ESRD. The results of this study support further investigations into the optimal dosing strategies specifically tailored for this patient demographic.

Robust testing for explosive behavior with strongly dependent errors

A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite sample performance of the proposed test and estimators. An empirical application to the S&P 500 index highlights the usefulness of the proposed procedures in practical work.

Forest resilience research using remote sensing and GIS – A systematic literature review

Forest resilience assessment is increasingly important given the current global environmental change. However, attributes and indicators to quantify forest resilience still need to be explored. Remote sensing (RS) and Geographical Information System (GIS) techniques are widely applied for forest resilience modeling. A bibliometric analysis was conducted to obtain insights concerning methods for quantifying forest resilience using RS/GIS. VosViewer and Bibliometrix R software were applied to analyze 117 articles from the Web of Science global database covering a period of 2011-2021. Using inclusion-exclusion criteria, 31 studies were examined, covering local, regional, and transnational ecosystem types. Satellite devices were used in 28 studies, whilst GIS dataset frameworks were used in the remaining studies. Multiple satellites and sensors were preferable to maximize results for modeling forest resilience. To estimate resilience, ecological attributes (above-ground biomass, tree-ring, and basal area increments) and remote-sensing derived indicators (vegetation indices, forest cover changes, deforestation rates, and forest productivity) were analyzed using conventional statistical tests or machine learning techniques. Studies combined experiments, observations, and process-based models demonstrated better results. Scale and resolution, indicator uncertainty, and data availability were among the constraints reported using RS/GIS. Therefore, a standardized framework for forest resilience assessment incorporating field observation with RS/GIS is needed.

Statistical Fragility of Randomized Controlled Trials Evaluating Rehabilitation After Arthroscopic Rotator Cuff Repair.

Clinical decision-making often relies on evidence-based medicine, derived from objective data with conventional and rigorous statistical tests to evaluate significance. The literature surrounding rehabilitation after rotator cuff repair (RCR) is conflicting, with no defined standard of practice. To determine the fragility index (FI) and the fragility quotient (FQ) of randomized controlled trials (RCTs) evaluating rehabilitation protocols after RCR. Systematic review. A systematic review was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines by searching the PubMed, Cochrane Library, and Embase databases for RCTs evaluating rehabilitation protocols after arthroscopic RCRs from 2000 to June 1, 2022. The FI was determined by manipulating the dichotomous outcome events from each article until a reversal of significance with 2 × 2 contingency tables was achieved. The FQ was determined by dividing the FI by the sample size. Fourteen RCTs with 48 dichotomous outcomes were ultimately included for analysis. The mean FI for the included dichotomous outcomes was 4 (interquartile range, 3-6), suggesting that the reversal of 4 events is required to change study significance. The mean FQ was 0.048. Of the RCTs that reported data regarding loss to follow-up, most studies (58.5%) indicated that >4 patients had been lost to follow-up. The results of RCT studies of RCR rehabilitation protocols are moderately fragile, something clinicians should be aware of when implementing study results into practice. We recommend the inclusion of FI and FQ in addition to standard P values when reporting statistical results in future RCTs with dichotomous outcome variables on this topic.

Severe testing of Benford’s law

Benford’s law is often used to support critical decisions related to data quality or the presence of data manipulations or even fraud in large datasets. However, many authors argue that conventional statistical tests will reject the null of data “Benford-ness” if applied in samples of the typical size in this kind of applications, even in the presence of tiny and practically unimportant deviations from Benford’s law. Therefore, they suggest using alternative criteria that, however, lack solid statistical foundations. This paper contributes to the debate on the “large n” (or “excess power”) problem in the context of Benford’s law testing. This issue is discussed in relation with the notion of severity testing for goodness-of-fit tests, with a specific focus on tests for conformity with Benford’s law. To do so, we also derive the asymptotic distribution of the mean absolute deviation (MAD) statistic as well as an asymptotic standard normal test. Finally, the severity testing principle is applied to six controversial large datasets to assess their “Benford-ness”.

Robust Inference on Infinite and Growing Dimensional Time‐Series Regression

We develop a class of tests for time‐series models such as multiple regression with growing dimension, infinite‐order autoregression, and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank p. Employing such increasing p asymptotics, we introduce a new scale correction to conventional test statistics, which accounts for a high‐order long‐run variance (HLV), which emerges as p grows with sample size. We also propose a bias correction via a null‐imposed bootstrap to alleviate finite‐sample bias without sacrificing power unduly. A simulation study shows the importance of robustifying testing procedures against the HLV even when p is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).

Test for equality of standardized generalized variance with different dimensions under high-dimensional settings

In this article, the testing equality of the standardized generalized variance (SGV) of k multivariate normal distributions with possibly unequal dimensions is studied. The conventional likelihood-ratio test statistic reveals a serious bias with an increase in dimensions. Therefore, we present a new test statistic that eliminates this bias, and propose an asymptotic approximation-based test. Our proposed test is valid not only in high-dimensional settings but also in large sample settings. Additionally, we obtain the asymptotic non-null distribution of the proposed test and the approximate confidence interval of the SGV under high-dimensional and large sample settings. Finally, we investigate the finite sample and dimension behavior of this test using Monte Carlo simulations.

The multivariate analysis of variance as a powerful approach for circular data

BackgroundA broad range of scientific studies involve taking measurements on a circular, rather than linear, scale (often variables related to times or orientations). For linear measures there is a well-established statistical toolkit based on linear modelling to explore the associations between this focal variable and potentially several explanatory factors and covariates. In contrast, statistical testing of circular data is much simpler, often involving either testing whether variation in the focal measurements departs from circular uniformity, or whether a single explanatory factor with two levels is supported.MethodsWe use simulations and example data sets to investigate the usefulness of a MANOVA approach for circular data in comparison to commonly used statistical tests.ResultsHere we demonstrate that a MANOVA approach based on the sines and cosines of the circular data is as powerful as the most-commonly used tests when testing deviation from a uniform distribution, while additionally offering extension to multi-factorial modelling that these conventional circular statistical tests do not.ConclusionsThe herein presented MANOVA approach offers a substantial broadening of the scientific questions that can be addressed statistically using circular data.

The Mansurov effect: Statistical significance and the role of autocorrelation

The Mansurov effect is related to the interplanetary magnetic field (IMF) and its ability to modulate the global electric circuit, which is further hypothesized to impact the polar troposphere through cloud generation processes. We investigate the connection between IMFBy-component and polar surface pressure by using daily ERA5 reanalysis for geopotential height since 1980. Previous studies produce a 27-day cyclic response during solar cycle 23 which appears to be significant according to conventional statistical tests. However, we show here that when statistical tests appropriate for strongly autocorrelated variables are applied, there is a fairly high probability of obtaining the cyclic response and associated correlation merely by chance. Our results also show that data from three other solar cycles produce similar cyclic responses as during solar cycle 23, but with seemingly random offset in respect to the timing of the signal. By generating random normally distributed noise with different levels of temporal autocorrelation and using the real IMFBy-time series as forcing, we show that the methods applied to support the Mansurov hypothesis up to now are highly susceptible to random chance as cyclic patterns always arise as artifacts of the methods. The potential non-stationary behavior of the Mansurov effect makes it difficult to achieve solid statistical significance on decadal time scales. We suggest more research on, e.g., seasonal dependence of the Mansurov effect to understand better potential IMF effects in the atmosphere.

Cryptanalysis of a Chaotic Block Cryptographic System Against Template Attacks

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. [Formula: see text]32% less attack traces than correlation power analysis, when the templates are properly built.

Simple and trustworthy cluster-robust GMM inference

This paper develops a new asymptotic theory for GMM estimation and inference in the presence of clustered dependence. The key feature of our alternative asymptotics is that the number of clusters G is regarded as fixed as the sample size increases. Under the fixed-G asymptotics, we show that the Wald and t tests in two-step GMM are asymptotically pivotal only if we recenter the estimated moment process in the clustered covariance estimator (CCE). Also, the J statistic, the trinity of two-step GMM statistics (QLR, LM, and Wald), and the t statistic can be modified to have an asymptotic standard F distribution or t distribution. We suggest a finite-sample variance correction to further improve the accuracy of the F and t approximations. The proposed tests are very appealing to practitioners because the test statistics are simple modifications of conventional GMM test statistics, and critical values are readily available from F and t tables. No further simulations or resampling methods are needed. A Monte Carlo study shows that our proposed tests are more accurate than the conventional large-G asymptotic inferences.

A Note on the Promising Zone Approach in Adaptive Trial Design

The promising zone approach in adaptive trial design has gained popularity since its inception due to the use of the conventional test statistic at the final analysis stage rather than a weighted version that seemingly penalizes the second stage data when the adaptive trial strategy activates a sample size increase. However, this perceived advantage suffers loss of efficiency. This article is to show through mathematical derivation that the weighted test statistic by Cui, Huang, and Wang (CHW) is uniformly more powerful than the Mehta and Pocock method in the statistical promising zone. Due to relatively small chance to fall into the statistical promising zone, in practice, a wider zone defined by the minimally clinically meaningful treatment effect and the target conditional power could be used. The proposed approach is a hybrid approach by which the CHW test statistic is applied within a chosen promising zone (could be wider than the statistical promising zone), outside which the conventional test statistic is applied without a sample size increase. Simulation studies are recommended to facilitate the application of this hybrid approach.

A tail-based test to detect differential expression in RNA-sequencing data.

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.

Generative modeling of brain maps with spatial autocorrelation

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

Factors predicting satisfaction in outpatient substance abuse treatment: a prospective follow-up study

BackgroundWhile treatment satisfaction has been associated with better outcomes in substance abuse treatment, there is an obvious need for a more profound understanding of what predicts client’s satisfaction with treatment. This study elucidates factors relevant to treatment outcome measured at follow-up in terms of satisfaction with the treatment received.MethodsThe research was implemented as a multisite study in outpatient clinics (N = 7) in southern and western Finland. Data consists of therapists (N = 33) and their clients (N = 327). Each consenting client beginning a treatment period was accepted as a research subject and all therapists at the clinics in question participated. The study was conducted as part of the clinic’s normal activity. Clients were allocated to therapists according to a randomization list drawn up in advance. Apart from the randomisation and the completion of questionnaires, it did not interfere with the progress of treatment. Follow-up lasted 6 months. Multiple Classification Analysis (MCA) was used through combinations of variables organized by content, e.g. client demographics, previous substance use, therapist’s characteristics and client’s expectations. The analyses were based in part on conventional statistical testing (t -test, χ2-test, ANOVA).ResultsAmong 37 independent variables few were statistically significant in the final model. The results suggest that high treatment expectations at baseline are a strong predictor of satisfaction at follow-up. Also, previous substance use predicted treatment satisfaction; people using multiple substances were less satisfied than those taking only one substance. Stronger predictors reduced the statistical significance of those independent variables that were statistically significant in the first analyses. Therefore, therapist’s role in recovery and readiness to change should be also seen as antecedents to treatment satisfaction.ConclusionsIt seems that treatment expectations are fulfilled among those participating in follow-up. Yet many are lost during treatment and by follow-up. Service users have experiential knowledge that differs from professionals’ and policymakers’ knowledge. It is clinically relevant to understand what factors affect client’s satisfaction. Hence, it is possible to identify the population whose treatment should receive the most attention, how the client’s experience, their commitment to treatment, and treatment effectiveness could be improved.

A New Robust Inference for Predictive Quantile Regression

For predictive quantile regressions with highly persistent regressors, a conventional test statistic suffers from a serious size distortion and its limiting distribution relies on the unknown persistence degree of predictors. This paper proposes a double-weighted approach to offer a robust inferential theory across all types of persistent regressors. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor. Under some mild conditions, it shows that the self-normalized test statistic based on the weighted estimator converges to a standard normal distribution. Our new approach enjoys a nice property that it can reach the local power under the optimal rate T with nonstationary predictor and squared root of T for stationary predictor, respectively. More importantly, our approach can be easily used to characterize mixed persistence degrees in multiple regressions. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogenous predictability of US stock returns at different quantile levels is reexamined.

Effect of the Parking Lane Configuration on Vehicle Speeds in Home Zones in Poland

Nowadays, traffic calming measures that are designed to influence the drivers’ behaviour in the first place and thus make them slow down (and increase the safety of traffic as a result) are used increasingly often in traffic engineering. As a rule of thumb, carefully planned street geometry and street furniture should advise the drivers of the traffic calmed area ahead of them, which makes them reduce the vehicle speed by influencing their perception. One of the most common treatments are road narrowings and horizontal deflections of the route of travel. The reduction of the vehicle speed increases the driver’s central visual area, which results in earlier and easier spotting of pedestrians that are about to cross the roadway. A reduction of noise and exhaust emissions is an additional benefit. However, the current sustainable design guidelines are not specific regarding how often the route should be deflected by alternate parking lanes and how frequently they should be placed in the home zones (woonerven in Dutch) in order to achieve the desired reduction of vehicle speed, noise, pollution, and exhaust emissions. This being so, the authors carried out a speed survey research on a chosen street that includes woonerf design features, as typically used in Poland, with carriageway narrowings being created by parking lanes differently sited along its length. Several speed measuring devices were deployed to automatically and simultaneously measure and record the vehicle speeds and volume of traffic at different test locations. The measurement data were subjected to statistical analyses, including conventional statistical tests. The alternative hypothesis, proposing that the vehicle speeds depend on the configuration of parking lanes and carriageway narrowings was confirmed in almost all cases. The results have confirmed that the siting of parking lanes is a relevant factor as far as speed reduction is concerned, with the degree of this reduction depending on the remaining travel lane width.

On Inference When Using State Corporate Laws for Identification

A popular research design identifies the effects of corporate governance by (changes in) state laws, clustering standard errors by state of incorporation. Using Monte-Carlo simulations, this paper shows that conventional statistical tests based on these standard errors dramatically overreject: in a typical design, randomly generated “placebo laws” are “significant” at the 1/5/10% level 9/21/30% of the time. This poor coverage is due to the extremely unequal cluster sizes, especially Delaware's concentration of half of all incorporations. Fixes recommended in the literature fail, including degrees-of-freedom corrections and the cluster wild bootstrap. The paper proposes a permutation test for valid inference.