The Assessment Checklist for Adolescents — ACA: A scale for measuring the mental health of young people in foster,kinship, residential and adoptive care

The Assessment Checklist for Adolescents — ACA is a 105-item carer-report mental health rating scale, measuring behaviours, emotional states, traits, and manners of relating to others, as manifested by adolescents (ages 12 to 17) residing in various types of alternate care, as well as those adopted from care. The ACA was designed for population and clinical research with these young people, and for use as a clinical assessment measure. The ACA's content was largely derived from the Assessment Checklist for Children (ACC). Fifteen ACC items were modified to better reflect adolescent difficulties, and 25 additional items were derived using a combination of inductive and deductive strategies. Item and factor analyses were carried out on scores from a 136-item research instrument, obtained for 230 young people residing in long-term care (as part of the NSW Children in Care study). These data were supplemented by ACC scores obtained for 142 adolescents residing in treatment foster care in Ontario, Canada. A robust 7-factor model was identified among a core of 73 clinical item scores, accounting for 51% of score variance. Four of the factors replicate ACC clinical scales (non-reciprocal interpersonal behaviour; sexual behaviour problems; food maintenance behaviour; and suicide discourse), and three are unique to the ACA (social instability/behavioural dysregulation; emotional dysregulation/distorted social cognition; and dissociation/trauma symptoms). The ACA also contains two empirically-derived low self-esteem scales (low confidence; negative self-image) that are shared with the ACC. Initial data indicate that the ACA has good content, construct and criterion-related validity, as well as high internal reliability.     

Empirical Likelihood for Non-Smooth Criterion Functions

Abstract.  Suppose that X ₁ ,…,  X _n is a sequence of independent random vectors, identically distributed as a d -dimensional random vector X . Let     be a parameter of interest and     be some nuisance parameter. The unknown, true parameters ( μ ₀ , ν ₀ ) are uniquely determined by the system of equations E { g ( X , μ ₀ , ν ₀ )} =   0 , where g  =  ( g ₁ ,…, g _{p + q} ) is a vector of p + q functions. In this paper we develop an empirical likelihood (EL) method to do inference for the parameter μ ₀ . The results in this paper are valid under very mild conditions on the vector of criterion functions g . In particular, we do not require that g ₁ ,…, g _{p + q} are smooth in μ or ν . This offers the advantage that the criterion function may involve indicators, which are encountered when considering, e.g. differences of quantiles, copulas, ROC curves, to mention just a few examples. We prove the asymptotic limit of the empirical log-likelihood ratio, and carry out a small simulation study to test the performance of the proposed EL method for small samples.     

王宠惠与民国初年的宪政热潮

宪政运动伴随着中华民族争取独立、民主、富强的斗争而在民国初年蓬勃展开,朝野各界纷纷以各种不同方式投入到这场立宪洪流之中,王宠惠作为近代中国有影响的法学家和政治活动家,与时参与了民国初年的联省自治思潮、国民制宪思潮和“好政府主义”思潮等各种宪政思潮,表达了通过建构宪政秩序促进国家走上现代化发展道路的进步理念。     

Confidence limits for stress–strength reliability involving Weibull models

The problem of interval estimation of the stress–strength reliability involving two independent Weibull distributions is considered. An interval estimation procedure based on the generalized variable (GV) approach is given when the shape parameters are unknown and arbitrary. The coverage probabilities of the GV approach are evaluated by Monte Carlo simulation. Simulation studies show that the proposed generalized variable approach is very satisfactory even for small samples. For the case of equal shape parameter, it is shown that the generalized confidence limits are exact. Some available asymptotic methods for the case of equal shape parameter are described and their coverage probabilities are evaluated using Monte Carlo simulation. Simulation studies indicate that no asymptotic approach based on the likelihood method is satisfactory even for large samples. Applicability of the GV approach for censored samples is also discussed. The results are illustrated using an example.     

Detection and Localization in Test Accuracy: A Bayesian Perspective

In assessing the area under the ROC curve for the accuracy of a diagnostic test, it is imperative to detect and locate multiple abnormalities per image. This approach takes that into account by adopting a statistical model that allows for correlation between the reader scores of several regions of interest (ROI).

The ROI method of partitioning the image is taken. The readers give a score to each ROI in the image and the statistical model takes into account the correlation between the scores of the ROI's of an image in estimating test accuracy. The test accuracy is given by Pr[Y > Z] + (1/2)Pr[Y = Z], where Y is an ordinal diagnostic measurement of an affected ROI, and Z is the diagnostic measurement of an unaffected ROI. This way of measuring test accuracy is equivalent to the area under the ROC curve. The parameters are the parameters of a multinomial distribution, then based on the multinomial distribution, a Bayesian method of inference is adopted for estimating the test accuracy.

Using a multinomial model for the test results, a Bayesian method based on the predictive distribution of future diagnostic scores is employed to find the test accuracy. By resampling from the posterior distribution of the model parameters, samples from the posterior distribution of test accuracy are also generated. Using these samples, the posterior mean, standard deviation, and credible intervals are calculated in order to estimate the area under the ROC curve. This approach is illustrated by estimating the area under the ROC curve for a study of the diagnostic accuracy of magnetic resonance angiography for diagnosis of arterial atherosclerotic stenosis. A generalization to multiple readers and/or modalities is proposed.

A Bayesian way to estimate test accuracy is easy to perform with standard software packages and has the advantage of employing the efficient inclusion of information from prior related imaging studies.     

Consistency of non parametric estimators of the area under the ROC curve

ABSTRACT

The area under the receiver operating characteristic (ROC) curve is a popular summary index that measures the accuracy of a continuous-scale diagnostic test to measure its accuracy. Under certain conditions on estimators of distribution functions, we prove a theorem on strong consistency of the non parametric “plugin” estimators of the area under the ROC curve. Based on this theorem, we construct some new “plugin” consistent estimators. The performance of the non parametric estimators considered is illustrated numerically and the estimators are compared in terms of bias, variance, and mean square error.     

Nonparametric Predictive Inference for diagnostic accuracy

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. Good methods for determining diagnostic accuracy provide useful guidance on selection of patient treatment, and the ability to compare different diagnostic tests has a direct impact on quality of care. In this paper Nonparametric Predictive Inference (NPI) methods for accuracy of diagnostic tests with continuous test results are presented and discussed. For such tests, Receiver Operating Characteristic (ROC) curves have become popular tools for describing the performance of diagnostic tests. We present the NPI approach to ROC curves, and some important summaries of these curves. As NPI does not aim at inference for an entire population but instead explicitly considers a future observation, this provides an attractive alternative to standard methods. We show how NPI can be used to compare two continuous diagnostic tests.     

Empirical likelihood confidence regions for comparison distributions and roc curves

Abstract: The authors derive empirical likelihood confidence regions for the comparison distribution of two populations whose distributions are to be tested for equality using random samples. Another application they consider is to ROC curves, which are used to compare measurements of a diagnostic test from two populations. The authors investigate the smoothed empirical likelihood method for estimation in this context, and empirical likelihood based confidence intervals are obtained by means of the Wilks theorem. A bootstrap approach allows for the construction of confidence bands. The method is illustrated with data analysis and a simulation study.     

The South Oaks Gambling Screen-revised Adolescent (SOGS-RA) Revisited: A Cut-point Analysis

The lack of a criterion validity gold standard defining adolescent pathological gambling represents a major limitation of the adolescent gambling literature. The present study employed Receiver Operating Characteristic curve analysis (ROC) to examine the performance of the South Oaks Gambling Screen-Revised Adolescent (SOGS-RA) recommended cut-point of 4 against two-proxy gold standards: self-identified need for, or receipt of, help for gambling. Logistic regression analysis examined the correlates of self-identified need for help with gambling. The sample was comprised of 12990 adolescents from Atlantic Canada whose average age was 15 years and 50% of whom were male. The SOGS-RA performed as well or better with these proxy gold standards than with proxies used in previous studies. We concluded that the proxy gold standards based on self-identified need for or receipt of help represent a pragmatic solution to the lack of an adolescent-specific definition of problem or pathological gambling. Students in grade seven and SOGS-RA scores of 4 or greater were independent predictors of self-identifying a need for help.     

Selection and combination of biomarkers using ROC method for disease classification and prediction

Based on the SCAD penalty and the area under the ROC curve (AUC), we propose a new method for selecting and combining biomarkers for disease classification and prediction. The proposed estimator for the combination of the biomarkers has an oracle property; that is, the estimated combination of the biomarkers performs as well as it would have been if the biomarkers significantly associated with the outcome had been known in advance, in terms of discriminative power. The proposed estimator is computationally feasible, n^1/2‐consistent and asymptotically normal. Simulation studies show that the proposed method performs better than existing methods. We illustrate the proposed methodology in the acoustic startle response study. The Canadian Journal of Statistics 39: 324–343; 2011 © 2011 Statistical Society of Canada