True Correlation Research Articles

An unbalanced multidimensional latent effects-based logistic mixed model and GQL estimation for spatial binary data

Spatial correlation structure is the most essential tool in a spatial data analysis. However, the difficulty of modelling spatial correlations between two responses collected from two neighbouring locations is a challenge, when it is known that each of the responses may also be influenced by certain visible and/or invisible effects of other neighbouring locations. Further difficulties arise when one deals with spatial binary data as opposed to linear spatial data. In this paper, we resolve this correlation model issue for spatial binary data by using a mixed logits model approach where pair-wise correlations are computed by accommodating both within and between correlations for paired-responses. For inferences, we use the true correlation based generalized quasi-likelihood (GQL) approach. The asymptotic normality of the estimators of the main regression and random effects variance parameters are studied. The model and estimation methodology used are illustrated by a finite sample-based simulation study.

The Multidimensional Forced-Choice Format as an Alternative for Rating Scales

The Multidimensional Forced-Choice Format as an Alternative for Rating Scales

Modelling Burglary Susceptibility – An Expert Judgement Approach

Developing a model allow a better understanding of the nature of a complicated phenomenon. With advancement of tools and technology, model development has been applied widely to mimic the phenomena of interest, spatial or non-spatial wise, allowing a guided decision making to be made. In this paper, the phenomena of burglary vulnerability and susceptibility are modelled based on expert opinion input to create a model that imitates the expert profiling of burglary occurrences, which is dependent on individual expert wisdom and experience in handling the burglary investigation. Due to seriousness of burglary crime offences in Malaysia, especially the urban areas, a prediction model is needed to correlates the factor of crime and further estimates the spatial susceptibility to work hand in hand with other government initiatives in reducing crime. Eighteen (18) indicators and 63 sub-indicators has been identified to be significant in defining the susceptibility of burglary. Apart from input of rating and ranking of indicators and sub-indicators obtained from questionnaire distribution to expert in handling burglary, the geospatial based data were also incorporated into the model to add the element of spatial accuracy in susceptibility prediction. The geospatial data includes the distribution of burglary incidence from 2010 – 2016, the census data, the building footprint data and the demarcation area. For the collected questionnaire feedback, the procedure of Analytical Hierarchy Process (AHP) were adapted to determine the weight value considering the rating input of expert from the distributed questionnaire. The input of weight and scoring were applied to the corresponding spatial features and combined with the operation of weighted sum to yield the total burglary susceptibility of a place. The results of the model were validated with the real reported burglary frequency based on True Positive Rate correlation matrix. The model validation finds that the model have a sensitivity of 82% in classifying the burglary susceptibility of the building polygon inside the study area. However this model still requires some improvement as it is still lacking to perform the classification of incidence intensity correctly.

A comparison of correlation and regression approaches for multinomial processing tree models

Multinomial processing tree (MPT) models are a class of stochastic models for categorical data that have recently been extended to account for heterogeneity in individuals by assuming separate parameters per participant. These extensions enable the estimation of correlations among model parameters and correlations between model parameters and external covariates. The present study compares different approaches regarding their ability to estimate both types of correlations. For parameter–parameter correlations, we considered two Bayesian hierarchical MPT models – the beta-MPT approach and the latent-trait approach – and two frequentist approaches that fit the data of each participant separately, either involving a correction for attenuation or not (corrected and uncorrected individual-model approach). Regarding parameter-covariate correlations, we additionally considered the latent-trait regression. Recovery performance was determined via a Monte Carlo simulation varying sample size, number of items, extent of heterogeneity, and magnitude of the true correlation. The results indicate the smallest bias regarding parameter–parameter​ correlations for the latent-trait approach and the corrected individual-model approach and the smallest bias regarding parameter-covariate correlations for the latent-trait regression and the corrected individual-model approach. However, adequately recovering correlations of MPT parameters generally requires a sufficiently large number of observations and sufficient heterogeneity.

Correlation analysis of the x-ray and functional parameters from the results of chevron (Austin) osteotomy for hallux valgus

Hallux valgus is characterized by the appearance and growth of a painful “lump” in the region of the first metatarsophalangeal joint, the development of forefoot corns, and inability to choose the right shoes, which leads to a significant decrease in the quality of life of these patients. Corrective osteotomies that preserve the metatarsophalangeal joint, for example Austin (Chevron) osteotomy, are usually used for hallux valgus deformity of the I, II degrees. Radiography with the study of the hallux valgus angle (HVA), the intermetatarsal angle (IMA), the distal metatarsal articular angle (DMAA) is a research method that shows the true correlation between bone structures. The correlation between the radiological and functional indicators of osteotomy allows us to determine possible recommendations for indications for surgical treatment of Hallux valgus. Correlation shows that the largest correction of hallux valgus in older patients occurs due to a small adjustment of the angle of DMMA and HVA. IMA had the best correction after Austin osteotomy among patients of a younger age, then the HVA, and the DMMA had minimum correction according to the AOFAS rating scale (Kitaoka). The revealed correlations allow us to determine the correct tactics for the treatment of hallux valgus by identifying the benefits of Austin osteotomy.

Generalized estimating equations to estimate the ordered stereotype logit model for panel data.

By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category-specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables. In this article, we propose a generalized estimating equations (GEE) approach to estimate the ordered stereotype logit model for panel data based on working covariance matrices, which are not required to be correctly specified. A simulation study compares the performance of GEE estimators based on various working correlation matrices and working covariance matrices using local odds ratios. Estimation of the model is illustrated using a real-world dataset. The results from the simulation study suggest that GEE estimation of this model is feasible in medium-sized and large samples and that estimators based on local odds ratios as realized in this study tend to be less efficient compared with estimators based on a working correlation matrix. For low true correlations, the efficiency gains seem to be rather small and if the working covariance structure is too flexible, the corresponding estimator may even be less efficient compared with the GEE estimator assuming independence. Like for GEE estimators more generally, if the true correlations over time are high, then a working covariance structure which is close to the true structure can lead to considerable efficiency gains compared with assuming independence.

A Revisit to the Asymmetric Stochastic Volatility Models' Correlation Structures

The asymmetric stochastic volatility (ASV) models extend the stochastic volatility model (SV) by modeling the correlation between the asset return and its volatility. We prove by simulation studies that fitting the ASV models may infer erroneous estimations of the correlation coefficients. Even if the true return-volatility correlation structure is different from the ASV models' specifications, the ASV models constantly provide significant correlation coefficients far from zero. The misidentification of the correlation parameter can be a significant issue considering that in-conclusive empirical evidence regarding the asymmetry has been documented, and the ASV model can only represent the general asymmetry according to its return-volatility correlation specification. The incorporation of ex-post volatility helps the ASV model to identify the true return-volatility correlation. Empirical evidence on major U.S. equity market indexes verifies that ASV models with ex-post volatility obtain significantly different estimations of the correlation parameters compared to the benchmark ASV models. Moreover, the inclusion of the ex-post volatility also significantly improves the ASV models in return density forecasting. It is necessary to include the ex-post volatility when estimating the ASV model.

The Genotypic and Phenotypic Spectrum of BICD2 Variants in Spinal Muscular Atrophy.

The bicaudal D cargo adaptor 2 (BICD2) gene encodes a conserved cargo adaptor protein required for dynein-mediated transport. Inherited and de novo variants in BICD2 cause SMALED2 (spinal muscular atrophy lower extremity dominant 2), and a subset have recently been reported to cause severe, often lethal disease. However, a true genotype-phenotype correlation for BICD2 has not been performed, and cases described to date are scattered among at least 14 publications. In this review, we identify the characteristics of disease-causing variants in BICD2 that distinguish them from benign variation and perform genotype-phenotype correlations for 99 BICD2 variant carriers from 35 families. ANN NEUROL 2020;87:487-496.

Angular Correlation Function Estimators Accounting for Contamination from Probabilistic Distance Measurements

Abstract With the advent of surveys containing millions to billions of galaxies, it is imperative to develop analysis techniques that utilize the available statistical power. In galaxy clustering, even small sample contamination arising from distance uncertainties can lead to large artifacts, which the standard estimator for two-point correlation functions does not account for. We first introduce a formalism, termed decontamination, that corrects for sample contamination by utilizing the observed cross-correlations in the contaminated samples; this corrects any correlation function estimator for contamination. Using this formalism, we present a new estimator that uses the standard estimator to measure correlation functions in the contaminated samples but then corrects for contamination. We also introduce a weighted estimator that assigns each galaxy a weight in each redshift bin based on its probability of being in that bin. We demonstrate that these estimators effectively recover the true correlation functions and their covariance matrices. Our estimators can correct for sample contamination caused by misclassification between object types as well as photometric redshifts; they should be particularly helpful for studies of galaxy evolution and baryonic acoustic oscillations, where forward modeling the clustering signal using the contaminated redshift distribution is undesirable.

Information growth for sequential monitoring of clinical trials with a stepped wedge cluster randomized design and unknown intracluster correlation.

In a stepped wedge study design, study clusters usually start with the baseline treatment and then cross over to the intervention at randomly determined times. Such designs are useful when the intervention must be delivered at the cluster level and are becoming increasingly common in practice. In these trials, if the outcome is death or serious morbidity, one may have an ethical imperative to monitor the trial and stop before maximum enrollment if the new therapy is proven to be beneficial. In addition, because formal monitoring allows for the stoppage of trials when a significant benefit for new therapy has been ruled out, their use can make a research program more efficient. However, use of the stepped wedge cluster randomized study design complicates the implementation of standard group sequential monitoring methods. Both the correlation of observations introduced by the clustered randomization and the timing of crossover from one treatment to the other impact the rate of information growth, an important component of an interim analysis. We simulated cross-sectional stepped wedge study data in order to evaluate the impact of sequential monitoring on the Type I error and power when the true intracluster correlation is unknown. We studied the impact of varying intracluster correlations, treatment effects, methods of estimating the information growth, and boundary shapes. While misspecified information growth can impact both the Type I error and power of a study in some settings, we observed little inflation of the Type I error and only moderate reductions in power across a range of misspecified information growth patterns in our simulations. Taking the study design into account and using either an estimate of the intracluster correlation from the ongoing study or other data in the same clusters should allow for easy implementation of group sequential methods in future stepped wedge designs.

Optimizing design to estimate genetic correlations between environments with common environmental effects.

Breeding programs for different species aim to improve performance by testing members of full-sib (FS) and half-sib (HS) families in different environments. When genotypes respond differently to changes in the environment, this is defined as genotype by environment (G × E) interaction. The presence of common environmental effects within families generates covariance between siblings, and these effects should be taken into account when estimating a genetic correlation. Therefore, an optimal design should be established to accurately estimate the genetic correlation between environments in the presence of common environmental effects. We used stochastic simulation to find the optimal population structure using a combination of FS and HS groups with different levels of common environmental effects. Results show that in a population with a constant population size of 2,000 individuals per environment, ignoring common environmental effects when they are present in the population will lead to an upward bias in the estimated genetic correlation of on average 0.3 when the true genetic correlation is 0.5. When no common environmental effects are present in the population, the lowest standard error (SE) of the estimated genetic correlation was observed with a mating ratio of one dam per sire, and 10 offspring per sire per environment. When common environmental effects are present in the population and are included in the model, the lowest SE is obtained with mating ratios of at least 5 dams per sire and with a minimum number of 10 offspring per sire per environment. We recommend that studies that aim to estimate the magnitude of G × E in pigs, chicken, and fish should acknowledge the potential presence of common environmental effects and adjust the mating ratio accordingly.

Higher-order spatial correlation coefficients of ultrasonic backscattering signals using partial cross-correlation analysis.

The spatial correlation properties of ultrasonic backscattering signals in random media have important implications. For example, they can be used for microstructural characterization and flaw detection in engineering materials. However, the traditional spatial correlation coefficient (SCC) is only a leading order quantity that does not capture the true spatial correlations of random media. This is caused by neglecting confounding variables such as non-zero means or other non-zero odd-order moments. Here, the SCC is generalized from zeroth- to general-order through partial cross-correlation analysis. A series of indicators are defined to quantify the SCC curve at zero time lag, and the maximum time shift curve, which are both functions of lateral separation between two sensor positions. A stainless-steel specimen and a focused ultrasonic transducer are used to verify the method. Scattering measurements show that the higher-order SCC can consistently capture spatial correlations whereas the zeroth-order SCC is inadequate. The zeroth-order SCC is shown to predict a step size that can be more than six times too large. Thus, the present method can provide better understanding of statistical correlations and conditions to measure uncorrelated backscattering signals.

A methodology for improving efficiency estimation based on conditional mix-GEE models in longitudinal studies

Estimating random effects accurately is crucial since it reflects the subject-specific effect in longitudinal studies. In this paper, we develop a new methodology for improving the efficiency of fixed-effects and random-effects estimation based on conditional mix-GEE models. The advantage of our proposed approach is that the serial correlation over time was accommodated in estimating random effects. Meanwhile, the normality assumption for random effects is not required. In addition, according to the estimates of some mixture proportions, the true working correlation matrix can be identified. The feature of our proposed approach is that the estimators of the regression parameters are more efficient than CCQIF, cmix-GEE and CQIF approaches even if the working correlation structure is not correctly specified. In theory, we show that the proposed method yields a consistent and more efficient estimator than the random-effect estimator that ignores correlation information from longitudinal data. We establish asymptotic results for both fixed-effects and random-effects estimators. Simulation studies confirm the performance of our proposed method.

Embryonic/Fetal Development, Placentation and Glycosaminoglycans in the Female Reproductive Tract and Placenta

Background: Fetal attachments, placentation and embryonic development have been widely discussed in rodents such as agoutis and cavies, as well as research on glycosaminoglycans (GAGs) in rats and rabbits. Moreover, studies on buffalo, cattle and sheep are described in ruminants, and work has also been reported in sheep with GAGs in placentoma. However, further studies are needed in this regard, since there are reports of economic losses associated with reproductive failures described for cattle such as changes in the chorion and allantois, and in sheep in which changes between the transition from vitelline to allantois circulation have been discussed. Review: In relation to embryonic development, detailed studies have been described in rodents such as rats (12 days old), desert mouse (15 days old) and agoutis at 30 days. Macroscopic structures such as the cephalic region, nose, optic vesicle, cervical curvature, thoracic and pelvic limbs were observed, as well as microscopic structures such as the pituitary, lung, heart, brain cavity, liver, retina, and ossification regions. There are reports of buffalo and cattle studies in ruminants describing early embryonic development. However, the research in the case of sheep is limited, meaning there is only the ultrasound examination, such as gestational diagnosis and morphometric measurement of the embryonic vesicle. Still, studies with umbilical funicular and placental development of sheep with different gestational ages can be highlighted. Regarding extraembryonic annexes, four important structures which contribute to embryonic maintenance have been reported. These are called the chorion, amnion, allantois and yolk sac, respectively, and are responsible for originating the placenta, embryonic protection, collecting metabolic waste and early embryonic nutrition. In addition, correlating the annexes gives rise to the placentation process, which were described two models; the first is transient, called chorioviteline, and the second is called chorioallantoid, which represents the definitive model. Allied to the gestational process, the importance of glycosaminoglycans (GAGs) and proteoglycans are worth mentioning, as they are essential components of the extracellular matrix. They are related in the implantation process, tissue organization during gestation, and also in placental angiogenesis, as described in ruminants (i.e. involved in the vascular growth that accompanies the development of the placenta), which in turn causes an increase in blood flow in this organ, and constitutes a determining factor for fetal development. Conclusion: Such studies regarding the embryonic development of ruminants are still limited to sonographic description and some information is only available in the context of extraembryonic membranes. For glycosaminoglycans, chondroitin sulfate, dermatan sulfate, heparan sulfate and hyaluronic acid are identified in the reproductive tract and placenta. We intend to produce important information for the reproductive and sanitary management of ruminants with the information in this article, providing data to stimulate new studies aiming to minimize the occurrence of embryonic death and economic losses. In addition, further studies on GAGs are needed to better understand their true correlation with gestation, so they can intercede through supplementation and minimize reproductive losses.

The impact on estimations of genetic correlations by the use of super-normal, unscreened, and family-history screened controls in genome wide case-control studies.

Traditionally, in normal case-control studies of disorder A, the controls are defined as those not developing the disorder. However, in genome wide association (GWA) studies, controls are sometimes (a) unscreened or (b) screened for both disorder A and disorder B, producing super-normal controls. Using simulations, we examine how the observed genetic correlations between two disorders (A and B) are influenced by the use of unscreened, normal, and super-normal controls. Normal controls produce unbiased estimates of the genetic correlation. However, unscreened and super-normal controls both bias upward the genetic correlations. The strength of the bias increases with increasing population prevalences for the two disorders. With super-normal controls, the absolute magnitude of bias is stronger when the true genetic correlation is low. The opposite is seen with the use of unscreened controls. Adding screening of first-degree relatives of controls substantially increases the bias in genetic correlations with super-normal controls but has minimal impact when controls are screened only for the relevant disease.

Genetic characterization of Stargardt clinical phenotype in South Indian patients using sanger and targeted sequencing

BackgroundStargardt disease 1 (STGD1; MIM 248200) is a monogenic form of autosomal recessive genetic disease caused by mutation in ABCA4. This gene has a major role in hydrolyzing N-retinylidene-phosphatidylethanolamine to all-trans-retinal and phosphatidylethanolamine. The purpose of this study is to identify the frequency of putative disease-causing mutations associated with Stargardt disease in a South Indian population.MethodsA total of 28 clinically diagnosed Stargardt-like phenotype patients were recruited from south India. Ophthalmic examination of all patients was carefully carried out by a retina specialist based on the stages of fundus imaging and ERG grouping. Genetic analysis of ABCA4 was performed for all patients using Sanger sequencing and clinical exome sequencing.ResultsThis study identified disease-causing mutations in ABCA4 in 75% (21/28) of patients, 7% (2/28) exhibited benign variants and 18% (5/28) were negative for the disease-causing mutation.ConclusionThis is the first study describing the genetic association of ABCA4 disease-causing mutation in South Indian Stargardt 1 patients (STGD1). Our findings highlighted the presence of two novel missense mutations and an (in/del, single base pair deletion & splice variant) in ABCA4. However, genetic heterogeneity in ABCA4 mutants requires a larger sample size to establish a true correlation with clinical phenotype.

P1370 Correlation of aortic stenosis by integrated transthoracic and transesophageal echocardiography with calcium score

Abstract Funding Acknowledgements None BACKGROUND Aortic stenosis (AS) classification depends on left-ventricular ejection-fraction (LVEF <≥50%), aortic valve area (AVA<≥1cm2), mean pressure gradient (MG<≥40mmHg), peak velocity<≥400 cm/sec, and stroke-volume index (SVI<≥35ml/m2). Aortic Valve Agatston CT score (AVC) correlates with AS severity by trans-thoracic echo (TTE), but its association with AS severity determined by integrated TTE and TEE is unknown. PURPOSE We investigated correlation of AVC with dichotomous AS grouping by Integrated TTE + TEE vs TTE only. METHODS 64 TAVI candidates underwent sequential TTE and TEE, of which 24 underwent coronary CT within 4 months. Based on recommended conservative vs invasive treatment implication (A/B respectively), AS types were aggregated separately by TTE or Integrated TTE-TEE into two groups: Group-A (Moderate AS and Normal-Flow Low-Gradient), and Group-B (High-Gradient, Low-EF Low-Flow Low-Gradient, and Paradoxical Low-Flow Low-Gradient). Continuous and dichotomous AVC correlation (cutoffs based on guidelines) with echo binary classification was then determined. RESULTS Patients were 81.1(77.3-84.6) years old, 18(48.6%) were women, and had LVEF of 60% (49-65). AVC-score distribution in the two AS A/B Groups by two echo modalities is presented in the boxplot Figure. Only classification by TTE held discriminative accuracy in A/B grouping, with Area-Under-Curve of 0.736 (CI 0.57-0.9), and optimal threshold value of 1946 AU having 77% sensitivity and 74% specificity. Compared with AVC dichotomous classification, integrated TTE + TEE upgraded AS class (from A to B) in 5/6 (83.3%) patients vs 12/18 (66.7%) in which it downgraded AS class from B to A. CONCLUSIONS Aortic valve calcification correlates well with AS class dichotomized by operative implication through conventional TTE but not through integrated TTE + TEE. Our preliminary results appear to be caused by initial selection bias of patients in whom coronary CT performance was deemed to be justified by the treating physician rather than reflect a true better correlation between CT score and AS assessment by TTE vs by integrated TTE + TEE. Abstract P1370 Figure.

A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels

Feature selection has shown significant promise in improving the effectiveness of multi- label learning by constructing a reduced feature space. Previous studies typically assume that label assignment is complete or partially complete; however, missing-label and unlabeled data are commonplace and accompanying occurrences in real applications due to the high expense of manual annotation and label ambiguity. We call this “highly incomplete labels” problem. Such label incompleteness severely damages the inherent label structures and masks true label correlations. In this paper, we propose a novel structure-induced feature selection model to simultaneously identify the most discriminative features and recover the highly incomplete labels. To our best knowledge, it is the first attempt to explore the local density structure of data to capture the intricate feature-label dependency in the highly incomplete learning scenarios. Feature selection is guided by the label structure reconstruction, and highly incomplete labels are recovered via the structure transferred from feature space. In this elegant manner, the processes of selecting discriminative features and recovering incomplete labels are coupled in a unified optimization framework. Comprehensive experiments on public benchmark datasets demonstrate the superiority of the proposed approach.

Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

The temperature-sensitive point is the input variable of the thermal error compensation model of computer numerical control (CNC) machine tools. At present, the most commonly used selection method is to measure the multipoint temperature and thermal error of the machine tool synchronously and select several temperature measurement points with the highest thermal error correlation as temperature-sensitive points according to the measurement data. This study reveals that this method sometimes has obvious mis-selection and causes the model to fail. The reason is that the weak correlation temperature measurement point away from the machine tool heat source amplifies the volatility of the correlation evaluation result, owing to the small overall change. If the calculated result exceeds the true strong correlation temperature measurement point, it will be incorrectly selected as the temperature-sensitive point. This scenario has been termed herein as the pseudocorrelation problem. With the gradual popularization of thermal error compensation technology for CNC machine tools, pseudocorrelation will seriously affect the mass production pass rate. Therefore, the study analyzes and rigorously proves the mathematical mechanism of this problem, and the temperature measurement point preselection algorithm based on the correlation coefficient volatility determination factor (CCVDF) is proposed to eliminate potential pseudocorrelation temperature points before selecting temperature-sensitive points. After 1000 random simulation experiments, the failure rate of Z-direction thermal error modeling after preselection decreased from 5.1% to 0.5%, and the Y-direction error decreased from 33.7% to 1.5%. This algorithm can greatly improve the qualification rate of large-scale equipment of thermal error compensation technology.

Stop playing with data: there is no sound evidence that Bacille Calmette-Guérin may avoid SARS-CoV-2 infection (for now).

Since the beginning of the COVID-19 epidemic, a possible explanation for the high heterogeneity of infection/mortality rates across involved countries was hinted in the prevalence of tuberculosis vaccination with Bacille Calmette-Guérin (BCG). A systematic review was therefore performed on May 2, 2020. A total of 13 articles were ultimately retrieved, 12 of them as preprint papers. All articles were ecological studies of low quality. Most of them did not include main confounding factors (i.e. demographic of the assessed countries, share of people residing in urban settings, etc.), and simply assessed the differences among incidence/mortality of COVID-19 with vaccination rates or by having vs. having not any vaccination policy for BCG. Even though all studies shared the very same information sources (i.e. international registries for BCG vaccination rates and open source data for COVID-19 epidemics), results were conflicting, with later studies apparently denying any true correlation between COVID-19 occurrence and BCG vaccination rates and/or policies. As a consequence, there is no sound evidence to recommend BCG vaccination for the prevention of COVID-19. (www.actabiomedica.it)