RISK FACTORS ASSOCIATED WITH CULLING AGE IN DAIRY CATTLE: APPLICATIONS OF FRAILTY MODELS

Culling decisions for dairy cattle are an important component of dairy herd management. To investigate risk factors for culling, farms (clusters) constitute the sampling units. Therefore, we believe that ages-at-culling may be correlated within farms. The score test on the null hypothesis of no extra-variation in survival data was not supported by age-at-culling data collected from 72 dairy farms from the province of Ontario, Canada. To correct for the intraherd correlation, three modelling approaches were used to fit the data: Population-Averaged (PA) , cluster-specific (CS), and Random Effects Models (RAEM). The modelling approaches are described and compared using the dairy cow culling data

Copula based prediction models: an application to an aortic regurgitation study

<p>Abstract</p> <p>Background:</p> <p>An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed.</p> <p>Methods:</p> <p>The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the <it>Archimedean </it>copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure.</p> <p>Results:</p> <p>We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively.</p> <p>Conclusion:</p> <p>Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples.</p

Estimation of Parent-Sib Correlations for Quantitative Traits Using the Linear Mixed Regression Model: Applications to Arterial Blood Pressures Data Collected From Nuclear Families

A fundamental question in quantitative genetics is whether observed variation in the phenotypic values of a particular trait is due to environmental or to biological factors. Proportion of variations attributed to genetic factors is known as heritability of the trait. Heritability is a concept that summarizes how much of the variation in a trait is due to variation in genetic factors. Often, this term is used in reference to the resemblance between parents and their offspring. In this context, high heritability implies a strong resemblance between parents and offspring with regard to a specific trait, while low heritability implies a low level of resemblance. While many applications measure the offspring resemblance to their parents using the mid-parental value of a quantitative trait of interest as an input parameter, others focus on estimating maternal and paternal heritability. In this paper we address the problem of estimating parental heritability using the nuclear family as a unit of analysis. We derive moment and maximum likelihood estimators of parental heritability, and test their equality using the likelihood ratio test, the delta method. We also use Fieller’s interval on the ratio of parental heritability to address the question of bioequivalence. The methods are illustrated on published arterial blood pressures data collected from nuclear families

Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables

BACKGROUND: In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. METHODS: We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. RESULTS: For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on pre-specified width of confidence interval are novel contribution to the literature for the binary variable. CONCLUSION: Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary

Comparison of two dependent within subject coefficients of variation to evaluate the reproducibility of measurement devices

<p>Abstract</p> <p>Background</p> <p>The within-subject coefficient of variation and intra-class correlation coefficient are commonly used to assess the reliability or reproducibility of interval-scale measurements. Comparison of reproducibility or reliability of measurement devices or methods on the same set of subjects comes down to comparison of dependent reliability or reproducibility parameters.</p> <p>Methods</p> <p>In this paper, we develop several procedures for testing the equality of two dependent within-subject coefficients of variation computed from the same sample of subjects, which is, to the best of our knowledge, has not yet been dealt with in the statistical literature. The Wald test, the likelihood ratio, and the score tests are developed. A simple regression procedure based on results due to Pitman and Morgan is constructed. Furthermore we evaluate the statistical properties of these methods via extensive Monte Carlo simulations. The methodologies are illustrated on two data sets; the first are the microarray gene expressions measured by two plat- forms; the Affymetrix and the Amersham. Because microarray experiments produce expressions for a large number of genes, one would expect that the statistical tests to be asymptotically equivalent. To explore the behaviour of the tests in small or moderate sample sizes, we illustrated the methodologies on data from computer-aided tomographic scans of 50 patients.</p> <p>Results</p> <p>It is shown that the relatively simple Wald's test (WT) is as powerful as the likelihood ratio test (LRT) and that both have consistently greater power than the score test. The regression test holds its empirical levels, and in some occasions is as powerful as the WT and the LRT.</p> <p>Conclusion</p> <p>A comparison between the reproducibility of two measuring instruments using the same set of subjects leads naturally to a comparison of two correlated indices. The presented methodology overcomes the difficulty noted by data analysts that dependence between datasets would confound any inferences one could make about the differences in measures of reliability and reproducibility. The statistical tests presented in this paper have good properties in terms of statistical power.</p

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. Results: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

This study’s objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts’ classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN’s staging of TMJOA compared to the repeated clinicians’ consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN

Reproducibility of 3-dimensional ultrasound readings of volume of carotid atherosclerotic plaque

<p>Abstract</p> <p>Background</p> <p>Non-invasive 3-dimensional (3D) ultrasound (US) has emerged as the predominant approach for evaluating the progression of carotid atherosclerosis and its response to treatment. The aim of this study was to investigate the quality of a central reading procedure concerning plaque volume (PV), measured by 3D US in a multinational US trial.</p> <p>Methods</p> <p>Two data sets of 45 and 60 3D US patient images of plaques (mean PV, 71.8 and 39.8 μl, respectively) were used. PV was assessed by means of manual planimetry. The intraclass correlation coefficient (ICC) was applied to determine reader variabilities. The repeatability coefficient (RC) and the coefficient of variation (CV) were used to investigate the effect of number of slices (S) in manual planimetry and plaque size on measurement variability.</p> <p>Results</p> <p>Intra-reader variability was small as reflected by ICCs of 0.985, 0.967 and 0.969 for 3 appointed readers. The ICC value generated between the 3 readers was 0.964, indicating that inter-reader variability was small, too. Subgroup analyses showed that both intra- and inter-reader variabilities were lower for larger than for smaller plaques. Mean CVs were similar for the 5S- and 10S-methods with a RC of 4.7 μl. The RC between both methods as well as the CVs were comparatively lower for larger plaques.</p> <p>Conclusion</p> <p>By implementing standardised central 3D US reading protocols and strict quality control procedures highly reliable ultrasonic re-readings of plaque images can be achieved in large multicentre trials.</p

Frequency of GP communication addressing the patient's resources and coping strategies in medical interviews: a video-based observational study

<p>Abstract</p> <p>Background</p> <p>There is increasing focus on patient-centred communicative approaches in medical consultations, but few studies have shown the extent to which patients' positive coping strategies and psychological assets are addressed by general practitioners (GPs) on a regular day at the office. This study measures the frequency of GPs' use of questions and comments addressing their patients' coping strategies or resources.</p> <p>Methods</p> <p>Twenty-four GPs were video-recorded in 145 consultations. The consultations were coded using a modified version of the Roter Interaction Analysis System. In this study, we also developed four additional coding categories based on cognitive therapy and solution-focused therapy: attribution, resources, coping, and solution-focused techniques.</p> <p>The reliability between coders was established, a factor analysis was applied to test the relationship between the communication categories, and a tentative validating exercise was performed by reversed coding.</p> <p>Results</p> <p>Cohen's kappa was 0.52 between coders. Only 2% of the utterances could be categorized as resource or coping oriented. Six GPs contributed 59% of these utterances. The factor analysis identified two factors, one task oriented and one patient oriented.</p> <p>Conclusion</p> <p>The frequency of communication about coping and resources was very low. Communication skills training for GPs in this field is required. Further validating studies of this kind of measurement tool are warranted.</p