Likelihood Ratio Chi-square Statistic Research Articles

Baseline Electrocardiographic Abnormalities in Pre-Treatment Cancer Compared With Non-Cancer Patients: A Propensity Score Analysis.

Most studies have compared post-treatment electrocardiogram (ECG) abnormalities in cancer patients to the general population. To assess baseline cardiovascular (CV) risk, we compared pre-treatment ECG abnormalities in cancer patients with a non-cancer surgical population. We conducted a combined prospective (n = 30) and retrospective (n = 229) cohort study of patients aged 18 - 80 years with diagnosis of hematologic or solid malignancy, compared with 267 pre-surgical, non-cancer, age- and sex-matched controls. Computerized ECG interpretations were obtained, and one-third of the ECGs underwent blinded interpretation by a board-certified cardiologist (agreement r = 0.94). We performed contingency table analyses using likelihood ratio Chi-square statistics, with calculated odds ratios. Data were analyzed after propensity score matching. The mean age of cases was 60.97 ± 13.86; and 59.44 ± 11.83 years for controls. Pre-treatment cancer patients had higher likelihood of abnormal ECG (odds ratio (OR): 1.55; 95% confidence interval (CI): 1.05 to 2.30), and more ECG abnormalities (χ2 = 4.0502; P = 0.04) compared with non-cancer patients. ECG abnormalities were higher in black compared to non-black patients (P = 0.001). In addition, baseline ECGs among cancer patients prior to cancer therapy demonstrated less QT prolongation and intra-ventricular conduction defect (P = 0.04); but showed more arrhythmias (P < 0.01) and atrial fibrillation (AF) (P = 0.01) compared with the general patient population. Based on these findings, we recommend that all cancer patients receive an ECG, a low-cost and widely available tool, as part of their CV baseline screening, prior to cancer treatment.

Polynomial Columns-Parameter Symmetry Model and its Decomposition for Square Contingency Tables

This study proposes a polynomial columns-parameter symmetry model for square contingency tables with the same row and column ordinal classifications. In the proposed model, the odds for all i < j that an observation will fall in row category i and column category j instead of row category j and column category i depend on only the value of column category j . The proposed model is original because many asymmetry models in square contingency tables depend on the both values of row and column category. The proposed model constantly holds when the columns-parameter symmetry model holds; but the converse does not necessarily hold. This study shows that it is necessary to satisfy the polynomial columns-marginal symmetry model, in addition to the columns-parameter symmetry model, to satisfy the proposed model. This decomposition theorem is useful for explaining why the proposed model does not hold. Moreover, this study shows the value of likelihood ratio chi-square statistic for testing the proposed model is equal to the sum of that for testing the decomposed two models.

CCN family member 1 (CCN1) is an early marker of infarct size and left ventricular dysfunction in STEMI patients

Background and aimsCCN family member 1 (CCN1) has recently been proposed as a novel biomarker of myocardial injury, improving prediction of 30-day and one-year mortality following acute coronary syndromes. Among ST-elevation myocardial infarction (STEMI) patients, we evaluated the utility of CCN1 measured immediately before primary percutaneous coronary intervention (PPCI) as a predictor of two earlier endpoints: final myocardial infarct size and post-infarction left ventricular ejection fraction (LVEF). Furthermore, we evaluated the impact of CCN1 on the discriminatory power of the CADILLAC score. MethodsSTEMI patients were obtained from the SPUM-ACS cohort. Serum CCN1 was measured prior to PPCI. Linear regression assessed the association between CCN1, peak creatinine kinase (CK), and post-infarction LVEF. Cox models assessed an association between CCN1 and 30-day all-cause mortality. ResultsCCN1 was measured in 989 patients with a median value of 706.2 ng/l (IQR 434.3–1319.6). A significant correlation between CCN1, myocardial infarct size (peak CK) and LVEF was observed in univariate and multivariate analysis (both p < 0.001). Even among patients with normal classical cardiac biomarker levels at the time of PPCI, CCN1 correlated significantly with final infarct size. CCN1 significantly improved prediction of 30-day all-cause mortality by the CADILLAC score (C-index 0.864, likelihood-ratio chi-square test statistic 6.331, p = 0.012; IDI 0.026, p= 0.050). ConclusionsCompared with classical cardiac biomarkers, CCN1 is potentially the earliest predictor of final myocardial infarct size and post-infarction LVEF. CCN1 improved the discriminatory capacity of the CADILLAC score suggesting a potential role in the very-early risk stratification of STEMI patients.

Assessment of Teachers’ Knowledge about Tuberculosis at Primary School in Balad City

Tuberculosis (TB) is one of the most common infectious diseases worldwide and continues to be a major public health problem for low and middle-income countries. Undoubtedly, Lack of knowledge about tuberculosis among health care and education workers, as well as if knowledge and practices of tuberculosis among students were generally insufficient causes an increased risk of contracting the disease.Tuberculosis (TB) is a chronic communicable bacterial disease caused by Mycobacterium tuberculosis. The Latest World Health Organization (WHO) Report shows that there were 9.0 Million new TB cases and 1.5 Million tuberculosis deaths. The Transmission of the TB disease by Mycobacterium tuberculosis (a bacterium of a group that includes the causative agents of tuberculosis). takes place by air in the form of sneeze, talk, cough, spit, etc. [1,9,11,12,13] This applied study attempt to identify, assess and analyze teachers’ knowledge about tuberculosis in primary schools. A descriptive design, cross-sectional study was carried out in order to achieve the earlier stated objectives of this study by find out the relationship between teachers’ knowledge and social demographic data (sex, age, academic achievement, ….).The present study lasted for four months by prepared a questionnaires to assess the level of teachers’ knowledge, and these questionnaire contains many themes, each theme contained a number of questions to evaluate and analyze teachers’ knowledge of tuberculosis by answering a set of questions (as a variables); (mode of transmission, symptoms and signs, diagnostic features of TB, duration of treatment, prevention methods, risk of developing tuberculosis). The research hypothesis also states that (mycobacterium tuberculosis factor) has a direct impact on TB infection, and to achieve this hypothesis, a questionnaire was distributed to a sample with a size of (58) teachers and the method of Multiple Logistic Regression was used for statistical treatment. Finally, the research concluded a set of results and conclusions included in tables by comparing Likelihood-ratio chi-square statistics and classification table of the observed versus predicted responses.

Asymmetry model based on f-divergence and orthogonal decomposition of symmetry for square contingency tables with ordinal categories

Many statisticians have considered various symmetry and asymmetry models to analyze square contingency tables with ordinal categories. We propose a generalized model, which indicates the asymmetric structure for cell probabilities. This model is the closest to the symmetry model in terms of the f-divergence under certain conditions. Then we decompose the symmetry model using the proposed model, and partition the likelihood-ratio chi-square test statistics for the symmetry model. The decomposition is useful to deduce the reason for a poor fit of the symmetry model.

Sa1227 Long Term Outcomes of Crohn's Anastamotic Strictures Treated With Endoscopic Balloon Dilatation

was defined as first unplanned admission during this period. Readmission was defined as unplanned admission (due to any cause) occurring within 1 mon, 3 mons and 1 yr from the index admission. In order to identify factors predictive of readmissions, we compared social, demographic and clinical features at index admission of patients with readmission and those with no readmissions (Table 1 includes a partial list of variables assessed). Chi squared tests, t-tests and fisher exact tests were conducted to identify factors predictive of readmissions. Variables with univariate significance of less than 0.1 were included in the multivariate models. Only variables with significance less than 0.15 were retained in the final models. Model significance was assessed using the likelihood ratio chi-square statistic. Results: A total of 439 index admissions with primary discharge diagnosis of either UC or CD were eligible for inclusion in the study. These patients accounted for a total of 785 admissions to the health system during the study period. The unplanned readmission rates were14% at 1-mon, 23.7% at 3-mons, 39.2% at 1 yr. Demographics of the study cohort were: mean age 38 yrs, 55% females, 68% Whites, 144 had UC, 295 had CD, mean duration of IBD 10 yrs. Median LOS of pts with readmissions (6 days) was significantly longer than those without readmissions (5 days) at 1 months, 3 months and 1 yr (p=0.0001, p=0.003, p=0.003 respectively). Please refer to tables for the results. Conclusions: Based on our results we conclude that the rates of 1-month, 3-month and 1-yr unplanned readmissions rates are high in IBD patients. Receiving TPN, ICU stay predicted both early and late readmissions, while male sex, black race, having a government insurance and outside hospital transfers predicted 1 yr readmission. Being discharged on biologics was protective against 3-mons readmission. Since frequent admissions are indicators of poor quality of care, future prospective studies on large number of patients are needed to identify modifiable factors to prevent readmissions and thereby reduce healthcare costs. Risk factors for readmission on Uni-variate Analysis

Aspects of 3D surface scanner performance for post-mortem skin documentation in forensic medicine using rigid benchmark objects

Abstract Background : Patterned light 3D scanning has historically been targeted towards industrial and manufacturing applications. Forensic 3D skin surface scanning is relatively new and appears to contain aspects of off label usage. Based on how patterned light scanning has been published to work, we assumed that naturally rough surfaces' 3D scan validity to improve with extensive calibration of such a 3D-scanner whereas we assumed the same not to be true for industrially smooth surfaces. Using rigid benchmark objects matching aspects of typical post-mortem skin injuries and an object with smooth plastics surface, that hypothesis was tested. Methods : A 3D-scanner that captures stereoscopic images from patterned light was used. Impact of calibration extent on perceived differences between digital 3D models of industrially smooth and naturally rough objects was quantified with an experimental subjective comparison of 3D data appearance between minimal (MC) and extensive calibration (EC) against matched photos by 13 judges. Using extensive calibration, we then conducted (a) qualitative appreciation and (b) quantitative characterization of small surface regions to determine recognition rate of surface features. Results : Extensive calibration significantly improved the perceived quality of digitized naturally rough surfaces but turned out to not have a significant impact on the perceived quality of considerably smoother industrial surface digitization (G2 Likelihood Ratio Chi-square statistic p 0.0001 ). After calibrating the device exceeding manufacturer's recommendations, it adequately represented 98±1% of naturally rough surface contained features sized as small as 0.3mm within a user attended scan time of 8–12min per object. Conclusions : Significantly reduced apparent quality of patterned light scanner derived 3D models of rough surfaces may base on calibration recommendations that seem to be geared towards more efficient scanning of industrially smooth surfaces. As stripe pattern analysis typically includes non-linear approximations, a 3D scanner calibration process for rough detailed surfaces might benefit from as many small variations of distance and angles across the whole scanning volume as can be afforded by the user. Off label use entails reevaluation of devices for their intended new application.

Relation of Aortic Valve Calcium Detected by Cardiac Computed Tomography to All-Cause Mortality

Aortic valve calcium (AVC) can be quantified on the same computed tomographic scan as coronary artery calcium (CAC). Although CAC is an established predictor of cardiovascular events, limited evidence is available for an independent predictive value for AVC. We studied a cohort of 8,401 asymptomatic subjects (mean age 53 ± 10 years, 69% men), who were free of known coronary heart disease and were undergoing electron beam computed tomography for assessment of subclinical atherosclerosis. The patients were followed for a median of 5 years (range 1 to 7) for the occurrence of mortality from any cause. Multivariate Cox regression models were developed to predict all-cause mortality according to the presence of AVC. A total of 517 patients (6%) had AVC on electron beam computed tomography. During follow-up, 124 patients died (1.5%), for an overall survival rate of 96.1% and 98.7% for those with and without AVC, respectively (hazard ratio 3.39, 95% confidence interval 2.09 to 5.49). After adjustment for age, gender, hypertension, dyslipidemia, diabetes mellitus, smoking, and a family history of premature coronary heart disease, AVC remained a significant predictor of mortality (hazard ratio 1.82, 95% confidence interval 1.11 to 2.98). Likelihood ratio chi-square statistics demonstrated that the addition of AVC contributed significantly to the prediction of mortality in a model adjusted for traditional risk factors (chi-square = 5.03, p = 0.03) as well as traditional risk factors plus the presence of CAC (chi-square = 3.58, p = 0.05). In conclusion, AVC was associated with increased all-cause mortality, independent of the traditional risk factors and the presence of CAC.

On the comparison of the pre-test and shrinkage phi-divergence test estimators for the symmetry model of categorical data

The estimation problem of the parameters in a symmetry model for categorical data has been considered for many authors in the statistical literature (for example, Bowker (1948) [1], Ireland et al. (1969) [2], Quade and Salama (1975) [3], Cressie and Read (1988) [4], Menéndez et al. (2005) [5]) without using uncertain prior information. It is well known that many new and interesting estimators, using uncertain prior information, have been studied by a host of researchers in different statistical models, and many papers have been published on this topic (see Saleh (2006) [9] and references therein). In this paper, we consider the symmetry model of categorical data and we study, for the first time, some new estimators when non-sample information about the symmetry of the probabilities is considered. The decision to use a “restricted” estimator or an “unrestricted” estimator is based on the outcome of a preliminary test, and then a shrinkage technique is used. It is interesting to note that we present a unified study in the sense that we consider not only the maximum likelihood estimator and likelihood ratio test or chi-square test statistic but we consider minimum phi-divergence estimators and phi-divergence test statistics. Families of minimum phi-divergence estimators and phi-divergence test statistics are wide classes of estimators and test statistics that contain as a particular case the maximum likelihood estimator, likelihood ratio test and chi-square test statistic. In an asymptotic set-up, the biases and the risk under the squared loss function for the proposed estimators are derived and compared. A numerical example clarifies the content of the paper.

Comparison of 7 staging systems for patients with hepatocellular carcinoma undergoing transarterial chemoembolization

Many liver staging systems have been proposed for patients with hepatocellular carcinoma after locoregional therapy; however, controversies persist regarding which system is the best. In this study, the authors compared the performance of 7 staging systems in a cohort of patients with hepatocellular carcinoma who underwent transarterial chemoembolization. In total, 131 patients with hepatocellular carcinoma who underwent transarterial chemoembolization between August 1998 and February 2005 were included in the study. Demographic, laboratory, and tumor characteristics were determined at diagnosis and before therapy. At the time of censorship, 109 patients had died (83.2%). Predictors of survival were identified by using the Cox proportional hazards model. The likelihood-ratio chi-square statistic and the Akaike Information Criterion were calculated for 7 prognostic systems to evaluate their discriminatory ability. Comparisons of the survival rate between each stage were performed to evaluate the monotonicity of the gradients using Kaplan-Meier estimation and the log-rank test. The 5-year survival rate for the entire cohort was 13.6%. The independent predictors of survival were serum albumin level (<or=3.4 g/dL), the presence of ascites, serum alpha-fetoprotein level (>60 ng/mL), and portal or hepatic vein tumor thrombosis (P= .001, P= .001, P= .004, and P= .000, respectively). The Cancer of the Liver Italian Program classification system was superior to the other 6 prognostic systems regarding discriminatory ability and the monotonicity of the gradients. In this comparison of many staging systems, the Cancer of Liver Italian Program system provided the best prognostic stratification for a cohort the patients with hepatocellular carcinoma who underwent transarterial chemoembolization.

Higher-order approximations to the distributions of fit indexes under fixed alternatives in structural equation models

Higher-order approximations to the distributions of fit indexes for structural equation models under fixed alternative hypotheses are obtained in nonnormal samples as well as normal ones. The fit indexes include the normal-theory likelihood ratio chi-square statistic for a posited model, the corresponding statistic for the baseline model of uncorrelated observed variables, and various fit indexes as functions of these two statistics. The approximations are given by the Edgeworth expansions for the distributions of the fit indexes under arbitrary distributions. Numerical examples in normal and nonnormal samples with the asymptotic and simulated distributions of the fit indexes show the relative inappropriateness of the normal-theory approximation using noncentral chi-square distributions. A simulation for the confidence intervals of the fit indexes based on the normal-theory Studentized estimators under normality with a small sample size indicates an advantage for the approximation by the Cornish–Fisher expansion over those by the noncentral chi-square distribution and the asymptotic normality.

Calculation of Power in Chi-Square and Likelihood Ratio Chi-Square Statistics by a Special SAS Macro

Calculation of Power in Chi-Square and Likelihood Ratio Chi-Square Statistics by a Special SAS Macro

Comparison of the National Emergency Department Overcrowding Scale and the Emergency Department Work Index for Quantifying Emergency Department Crowding

Emergency department (ED) crowding is just beginning to be quantified. The only two scales presently available are the National Emergency Department Overcrowding Scale (NEDOCS) and the Emergency Department Work Index (EDWIN). To assess the value of the NEDOCS and the EDWIN in predicting overcrowding. The hypothesis of this study was that the NEDOCS and the EDWIN would be equally sensitive and specific for overcrowding. The NEDOCS, the EDWIN, and an overcrowding measure (OV) were determined every two hours for a ten-day period in December 2004. The NEDOCS is a statistically derived calculation, and the EDWIN is a formula-based calculation. The overcrowding measure is a composite of physician and charge nurse expert opinion on the degree of overcrowding as measured on a 100-mm visual analogue scale (VAS). The primary outcome, overcrowding, was based on the dichotomized OV VAS score at the midpoint of 50 mm (> or =50, overcrowded; <50, not overcrowded). The area under the receiver operator characteristic curve (AUC) and an index of adequacy (relative prognostic content) of each measure, on the basis of the likelihood ratio chi-square statistic, were computed to evaluate the performance of NEDOCS and EDWIN. There were 130 completed sampling times over ten days. The OV indicated that the ED was overcrowded 62% of the time. The AUC for the NEDOCS was 0.83 (95% CI = 0.75 to 0.90), and the AUC for the EDWIN was 0.80 (95% CI = 0.73 to 0.88). The NEDOCS score accounts for 97% of the prognostic information provided by combining all variables used in each model into one combined model. The EDWIN score accounts for only 86% (chi2 test for difference, p = 0.02). Both scales had high AUCs, correlated well with each other, and showed good discrimination for predicting ED overcrowding. This establishes construct validity for these scales as measures of overcrowding. Which scale is used in an ED is dependent on which set of data is most readily available, with the favored scale being the NEDOCS.

High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

Although the Bock–Aitkin likelihood-based estimation method for factor analysis of dichotomous item response data has important advantages over classical analysis of item tetrachoric correlations, a serious limitation of the method is its reliance on fixed-point Gauss-Hermite (G-H) quadrature in the solution of the likelihood equations and likelihood-ratio tests. When the number of latent dimensions is large, computational considerations require that the number of quadrature points per dimension be few. But with large numbers of items, the dispersion of the likelihood, given the response pattern, becomes so small that the likelihood cannot be accurately evaluated with the sparse fixed points in the latent space. In this paper, we demonstrate that substantial improvement in accuracy can be obtained by adapting the quadrature points to the location and dispersion of the likelihood surfaces corresponding to each distinct pattern in the data. In particular, we show that adaptive G-H quadrature, combined with mean and covariance adjustments at each iteration of an EM algorithm, produces an accurate fast-converging solution with as few as two points per dimension. Evaluations of this method with simulated data are shown to yield accurate recovery of the generating factor loadings for models of upto eight dimensions. Unlike an earlier application of adaptive Gibbs sampling to this problem by Meng and Schilling, the simulations also confirm the validity of the present method in calculating likelihood-ratio chi-square statistics for determining the number of factors required in the model. Finally, we apply the method to a sample of real data from a test of teacher qualifications.

Bootstrap tests for independence in two-way ordinal contingency tables

For the analysis of an r× c contingency table having ordered row categories and ordered column categories, a bootstrap method is applied for the model-based likelihood ratio test for independence. A model-based likelihood ratio chi-square statistic and the statistic of the maximum eigenvalue of a Wishart matrix are also discussed. A simulation study is performed to compare the proposed method with existing ones. A real data example is included.

Evaluation of two competing methods for calculating Charlson's comorbidity index when analyzing short-term mortality using administrative data

The performance and predictive power of the Deyo-Charlson and the Romano-Charlson comorbidity indices were compared when short-term mortality after hospitalization was the outcome of interest. These indices are commonly used to adjust for the effect of comorbidities when using administrative data in comparative studies. In-hospital Medicare claim data for patients admitted to one of six medical categories (back pain, stroke, pneumonia, hip replacement, transurethral radical prostatectomy, or lysis of peritoneal adhesion), were selected for analyses. Logistic regression models were employed to evaluate the relative importance and the explanatory power of these indices for predicting mortality 30, 90, and 180 days after admission. The contribution of each index to mortality was assessed via the likelihood ratio chi-square statistic (G 2), and the area under the receiver operator characteristic (ROC) curve was used to assess predictive power. Indices were evaluated using weights suggested by Charlson et al. and using empirically derived weights. Both indices improved the base model equally, although the predictive power of both indices was poor with values of the C statistic ranging from 0.60 to 0.78. Our results suggest limited applicability of these approaches when examining short-term mortality. A slight increase in predictive power was observed when indices were calculated using empirical weights derived from our data.

AX one degree of freedom nominal association model for testing independence in two-way contingency tables

In testing the independence of the row and column variables in a two-way contingency table, the standard Pearson and likelihood ratio chi-square statistics often have low power, especially as the dimensions of the table increase. In this paper, one degree of freedom tests of independence based on likelihood ratio and score statistics from an association model for tables with nominal row and column classifications are described. The score statistic is especially easy to use, since it can be expressed in closed form, is simple to compute, and has size and power properties which are only slightly inferior to those of the more complicated likelihood ratio statistic.

Direct comparison of bibliometric models

This study describes a technique for statistically comparing bibliometric models, and illustrates its use with three different examples. The technique is based on the idea of comparing full and restricted models as developed in analysis of variance, regression, and log-linear models. In bibliometrics, any two models where one is a special case of the other can be thought of as a full model and a restricted model. One can use the likelihood-ratio chi-square statistic, which has gained acceptance with log-linear models, as a test statistic to directly compare the full model and the restricted model. The first two examples involved Lotka's law. In the first example we investigated the feasibility of applying a single set of global parameter values to eight different author productivity distributions drawn from two different disciplines. In the second example we looked at whether or not a finite maximum productivity level was necessary as an additional parameter in Lotka-type models of author productivity. The final example compared three different forms of a model of library circulation frequencies.

Evidence of Item Bias in the Verbal Subtests of the WISC-R for Mexican-American Children

The study investigated cultural bias in the verbal items of the Wechsler Intelligence Scale for Children-Revised (WISC-R). Six verbal subtests of the WISC-R were administered to 40 Anglo and 40 Mexican-American subjects matched for grade level, sex, and socioeconomic status. The responses of two groups of subjects to individual items comprising verbal subtests were analyzed by log-linear technique using the likelihood ratio chi-square statistic. The findings revealed that the performance of subjects was homogeneous across two cultural groups on the items comprising Arithmetic, Comprehension, and Digit Span subtests. Ten (13%) of the 79 items comprising Information, Similarities, and Vocabulary subtests were, however, found to be biased against Mexican-American subjects. Three of these items are from the Information subtest, two from Similarities, and the remaining five items from Vocabulary. Implications of these findings for the psychoeducational assessment of minority children were discussed.

Interactive effects of item content and ethnic group membership on performance on the McCarthy scales

The study investigated cultural bias in the 46 verbal items of the McCarthy Scales of Children's Abilities (MSCA). The verbal items of the MSCA were administered to 59 Anglo and 59 Mexican-American subjects matched for age and sex. The responses of two groups of subjects on these items were analyzed by log-linear technique using the likelihood ratio chi-square statistic. The analysis of the data revealed that the majority of the verbal items of the MSCA are free from cultural bias. Only three items of the MSCA V-scale reflected systematic cultural bias. Two of these items are from the Word Knowledge II Subtest and the third one is from the Opposite Analogies Subtest. Suggestions for the use of the MSCA with children from minority cultures are made.