On estimating a constrained bivariate random effects model for meta-analysis of test accuracy studies

Tailored meta-analysis uses setting-specific knowledge for the test positive rate and disease prevalence to constrain the possible values for a test's sensitivity and specificity. The constrained region is used to select those studies relevant to the setting for meta-analysis using an unconstrained bivariate random effects model (BRM). However, sometimes there may be no studies to aggregate, or the summary estimate may lie outside the plausible or “applicable” region. Potentially these shortcomings may be overcome by incorporating the constraints in the BRM to produce a constrained model. Using a penalised likelihood approach we developed an optimisation algorithm based on co-ordinate ascent and Newton-Raphson iteration to fit a constrained bivariate random effects model (CBRM) for meta-analysis. Using numerical examples based on simulation studies and real datasets we compared its performance with the BRM in terms of bias, mean squared error and coverage probability. We also determined the ‘closeness’ of the estimates to their true values using the Euclidian and Mahalanobis distances. The CBRM produced estimates which in the majority of cases had lower absolute mean bias and greater coverage probability than the BRM. The estimated sensitivities and specificity for the CBRM were, in general, closer to the true values than the BRM. For the two real datasets, the CBRM produced estimates which were in the applicable region in contrast to the BRM. When combining setting-specific data with test accuracy meta-analysis, a constrained model is more likely to yield a plausible estimate for the sensitivity and specificity in the practice setting than an unconstrained model.

Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.

Geometric morphometric wing analysis as a tool to discriminate female mosquitoes from different suburban areas of Chiang Mai province, Thailand

Mosquitoes are hematophagous insects that transmit parasites and pathogens with devastating effects on humans, particularly in subtropical regions. Different mosquito species display various behaviors, breeding sites, and geographic distribution; however, they can be difficult to distinguish in the field due to morphological similarities between species and damage caused during trapping and transportation. Vector control methods for controlling mosquito-borne disease epidemics require an understanding of which vector species are present in the area as well as the epidemiological patterns of disease transmission. Although molecular techniques can accurately distinguish between mosquito species, they are costly and laborious, making them unsuitable for extensive use in the field. Thus, alternative techniques are required. Geometric morphometrics (GM) is a rapid and inexpensive technique that can be used to analyze the size, shape, and shape variation of individuals based on a range of traits. Here, we used GM to analyze the wings of 1,040 female mosquitoes from 12 different species in Thailand. The right wing of each specimen was removed, imaged microscopically, and digitized using 17 landmarks. Wing shape variation among genera and species was analyzed using canonical variate analysis (CVA), while discriminant function analysis was used to cross-validate classification reliability based on Mahalanobis distances. Phenetic relationships were constructed to illustrate the discrimination patterns for genera and species. CVA of the morphological variation among Aedes, Anopheles, Armigeres, Culex, and Mansonia mosquito genera revealed five clusters. In particular, we demonstrated a high percentage of correctly-distinguished samples among Aedes (97.48%), Armigeres (96.15%), Culex (90.07%), and Mansonia (91.67%), but not Anopheles (64.54%). Together, these findings suggest that wing landmark-based GM analysis is an efficient method for identifying mosquito species, particularly among the Aedes, Armigeres, Culex, and Mansonia genera.

Characteristics of the Donkey’s Dorsal Profile in Relation to Its Functional Body Condition Assessment

As the breeding of donkeys has increased due to different types of use, welfare evaluation importance increases. This equid’s welfare state has been described using body condition indicators and the geometric morphometrics method. However, the dorsal profile has not yet been assessed in donkeys. In this study, the body condition score (BCS), fatty neck score (FNS), dental condition score (DCS), sex, and breed were used as criteria of dorsal profile deformations. Photographs of 40 donkeys were analyzed using geometric morphometrics. Within the entire set of dorsal profiles, the variance of the first three principal components (PCs) was PC1 = 37.41%, PC2 = 23.43%, and PC3 = 13.34%. The dorsal profiles displayed deformation as an effect of FNS and BCS on size (FNS p = 0.012; BCS p = 0.024) and shape (FNS p < 0.0001; BCS p < 0.0001), rather than as an effect of DCS (p < 0.0001), sex (p = 0.0264), and breed (p < 0.0001) only on shape. The highest distances among the categories (Mahalanobis distances: MD ≥ 13.26; Procrustes distances: PD ≥ 0.044) were noted for FNS. The lowest distances were noted between jennets and males (MD = 4.58; PD = 0.012) and between BCS 1 and BCS 2 (MD = 4.70; PD = 0.018). Donkeys’ body condition affects their dorsal profile and both FNS and BCS measurements should be considered when a donkey’s dorsal profile is investigated.

A STUDY ON INTERNET DEPENDENCY, SOCIAL ISOLATION AND PERSONALITY USING MAHALANOBIS DISTANCE

With the advent of the globalised 21st century, hominids have metamorphosed to the credence and dependence on the internet. From education to shopping, there is a cumbersome dependence on this media. Yet, this rapid chrysalis of the Internet is unfortunate enough on the other side, since it bears a large impact on the human personality, coercing people to isolate from the social world. The present study accords with comparison among different psychological aspects of postgraduate level students with the help of Mahalanobis Distance. Different variables viz. Internet Dependency, Social Isolation and five different factors of Personality are considered for this study. Different groups like science and arts, male and female are formed for this study. Seven dependent variables like Internet Dependency, Social Isolation, Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness are taken as a branch for two dichotomous independent variables sex (male and female) and stream (arts and science) and Mahalanobis Distances are measured. It can be wrapped up on this note with a conclusion that there are no significant differences in dynamical nature between male and female students and arts and science students. KEYWORDS: Internet Dependency, Social Isolation, PG Level Students and Mahalanobis Distance.

Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków

The analysis of a city’s spatial development, in terms of a location that meets the needs of its inhabitants, requires many approaches. The preliminary assessment of the collected material showed that there was real estate in the database whose price did not have market characteristics. For the correct formulation of the valuation model, it is necessary to detect and eliminate or reduce the impact of these properties on the valuation results. In this study, multivariate analysis was used and three methods of detecting outliers were verified. The database of 8812 residential premises traded on the primary market in Kraków was analyzed. In order to detect outliers, the following indices were determined: projection matrix, Mahalanobis distances, standardized chi test and Cook distances. Critical values were calculated based on the formulas proposed in the publication. The probability level was P = 0.95. The article shows that the selected methods of eliminating outliers—the methods of standardized residuals and the Cook’s distance method give similar regression models. Further analysis (with the use of classification tree methods) made it possible to distinguish zones that are homogeneous in terms of price dispersion. In these zones, a set of features influencing real estate prices were determined.

High-Dimensional Mahalanobis Distances of Complex Random Vectors

In this paper, we investigate the asymptotic distributions of two types of Mahalanobis distance (MD): leave-one-out MD and classical MD with both Gaussian- and non-Gaussian-distributed complex random vectors, when the sample size n and the dimension of variables p increase under a fixed ratio c=p/n→∞. We investigate the distributional properties of complex MD when the random samples are independent, but not necessarily identically distributed. Some results regarding the F-matrix F=S2−1S1—the product of a sample covariance matrix S1 (from the independent variable array (be(Zi)1×n) with the inverse of another covariance matrix S2 (from the independent variable array (Zj≠i)p×n)—are used to develop the asymptotic distributions of MDs. We generalize the F-matrix results so that the independence between the two components S1 and S2 of the F-matrix is not required.

Discriminant Canonical Tool for Differential Biometric Characterization of Multivariety Endangered Hen Breeds

This study aimed to develop a tool to perform the morphological characterization of Sureña and Utrerana breeds, two endangered autochthonous breeds ascribed to the Mediterranean trunk of Spanish autochthonous hens and their varieties (n = 608; 473 females and 135 males). Kruskal–Wallis H test reported sex dimorphism pieces of evidence (p < 0.05 at least). Multicollinearity analysis reported (variance inflation factor (VIF) >5 variables were discarded) white nails, ocular ratio, and back length (Wilks’ lambda values of 0.191, 0.357, and 0.429, respectively) to have the highest discriminant power in female morphological characterization. For males, ocular ratio and black/corneous and white beak colors (Wilks’ lambda values of 0.180, 0.210, and 0.349, respectively) displayed the greatest discriminant potential. The first two functions explained around 90% intergroup variability. A stepwise discriminant canonical analysis (DCA) was used to determine genotype clustering patterns. Interbreed and varieties proximity was evaluated through Mahalanobis distances. Despite the adaptability capacity to alternative production systems ascribed to both avian breeds, Sureña and Utrerana morphologically differ. Breed dimorphism may evidence differential adaptability mechanisms linked to their aptitude (dual purpose/egg production). The present tool may serve as a model for the first stages of breed protection to be applicable in other endangered avian breeds worldwide.

Use of multivariate analysis as a tool in the morphological characterization of the main indigenous bovine ecotypes in northeastern Algeria

Sustainability in livestock farming requires monitoring of autochthonous breeds which are well adapted to the local environment. The morphometric measurements seem to be the first approach which can provide useful information on the suitability of animal genetic resources for selection. In this work, thirteen morphometric variables were used for the phenotypic characterization of 130 adult autochthones cattle randomly selected from 30 local farms in Guelma. There were cases from four commonly accepted and traditional ecotypes: Guelmois, Cheurfa, Sétifien and Fawn. The results showed several and significant positive correlations between the different variables. Correlations were analyzed using Varimax orthogonal rotation PCA and three factors were extracted, which explain more than 75% of the total variation in the four ecotypes. Stepwise discriminant analysis showed that 6 of the 13 variables had discriminatory power to define the phenotypic profile of the ecotypes. Canonical discriminant analysis indicated that the Sétifien ecotype is separate from the other three ecotypes. Mahalanobis distances were significant between the different ecotypes except for the distance between the Guelmois and Fawn ecotypes. The cross-validation procedure assigned 91.42% of the Sétifien animals to their genetic group, while the percentages of animals assigned to the Cheurfa, Guelmois and Fawn ecotypes were 80.00%, 65.71% and 53.33% respectively. The multivariate approach has proven to be effective in differentiating the four ecotypes, with clear morphological differences from the Sétifien ecotype that may benefit from a genetic improvement program for more sustainable genetic resources preservation.