A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

Multilevel analysis for a longitudinal clinical trial

This study applies the multilevel analysis technique to longitudinal data of a large clinical trial. The technique accounts for the correlation at different levels when modeling repeated blood pressure measurements taken throughout the trial. This modeling allows for closer inspection of the remaining correlation and non-homogeneity of variance in the data. Three methods of modeling the correlation were compared. ^

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

Structural equation modeling of the medical outcome study: Short Form 36 (SF-36)

The factorial validity of the SF-36 was evaluated using confirmatory factor analysis (CFA) methods, structural equation modeling (SEM), and multigroup structural equation modeling (MSEM). First, the measurement and structural model of the hypothesized SF-36 was explicated. Second, the model was tested for the validity of a second-order factorial structure, upon evidence of model misfit, determined the best-fitting model, and tested the validity of the best-fitting model on a second random sample from the same population. Third, the best-fitting model was tested for invariance of the factorial structure across race, age, and educational subgroups using MSEM.^ The findings support the second-order factorial structure of the SF-36 as proposed by Ware and Sherbourne (1992). However, the results suggest that: (a) Mental Health and Physical Health covary; (b) general mental health cross-loads onto Physical Health; (c) general health perception loads onto Mental Health instead of Physical Health; (d) many of the error terms are correlated; and (e) the physical function scale is not reliable across these two samples. This hierarchical factor pattern was replicated across both samples of health care workers, suggesting that the post hoc model fitting was not data specific. Subgroup analysis suggests that the physical function scale is not reliable across the "age" or "education" subgroups and that the general mental health scale path from Mental Health is not reliable across the "white/nonwhite" or "education" subgroups.^ The importance of this study is in the use of SEM and MSEM in evaluating sample data from the use of the SF-36. These methods are uniquely suited to the analysis of latent variable structures and are widely used in other fields. The use of latent variable models for self reported outcome measures has become widespread, and should now be applied to medical outcomes research. Invariance testing is superior to mean scores or summary scores when evaluating differences between groups. From a practical, as well as, psychometric perspective, it seems imperative that construct validity research related to the SF-36 establish whether this same hierarchical structure and invariance holds for other populations.^ This project is presented as three articles to be submitted for publication. ^

Forecasting cost based on a stochastic utilization plan and a fixed cost function

The application of Markov processes is very useful to health-care problems. The objective of this study is to provide a structured methodology of forecasting cost based upon combining a stochastic model of utilization (Markov Chain) and deterministic cost function. The perspective of the cost in this study is the reimbursement for the services rendered. The data to be used is the OneCare database of claim records of their enrollees over a two-year period of January 1, 1996–December 31, 1997. The model combines a Markov Chain that describes the utilization pattern and its variability where the use of resources by risk groups (age, gender, and diagnosis) will be considered in the process and a cost function determined from a fixed schedule based on real costs or charges for those in the OneCare claims database. The cost function is a secondary application to the model. Goodness-of-fit will be used checked for the model against the traditional method of cost forecasting. ^

Risk Index Score (RISc)

The main objective of this study was to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that can be translated into a simple scoring system in order to ascertain stroke cases using hospital admission medical records data. This algorithm, the Risk Index Score (RISc), was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christ (BASIC) project. The validity of the RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment accomplished by physician review of hospital admission records. The goal of this study was to develop a rapid, simple, efficient, and accurate method to ascertain the incidence of stroke from routine hospital admission hospital admission records for epidemiologic investigations. ^ The main objectives of this study were to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that could be translated into a simple scoring system to ascertain stroke cases using hospital admission medical records data. (Abstract shortened by UMI.)^

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^

Bayesian inference and its application to meta-analysis of epidemiologic data

In Part One, the foundations of Bayesian inference are reviewed, and the technicalities of the Bayesian method are illustrated. Part Two applies the Bayesian meta-analysis program, the Confidence Profile Method (CPM), to clinical trial data and evaluates the merits of using Bayesian meta-analysis for overviews of clinical trials.^ The Bayesian method of meta-analysis produced similar results to the classical results because of the large sample size, along with the input of a non-preferential prior probability distribution. These results were anticipated through explanations in Part One of the mechanics of the Bayesian approach. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Validity of using item response theory to analyze data from the Work Limitations Questionnaire

The Work Limitations Questionnaire (WLQ) is used to determine the amount of work loss and productivity which stem from certain health conditions, including rheumatoid arthritis and cancer. The questionnaire is currently scored using methodology from Classical Test Theory. Item Response Theory, on the other hand, is a theory based on analyzing item responses. This study wanted to determine the validity of using Item Response Theory (IRT), to analyze data from the WLQ. Item responses from 572 employed adults with dysthymia, major depressive disorder (MDD), double depressive disorder (both dysthymia and MDD), rheumatoid arthritis and healthy individuals were used to determine the validity of IRT (Adler et al., 2006).^ PARSCALE, which is IRT software from Scientific Software International, Inc., was used to calculate estimates of the work limitations based on item responses from the WLQ. These estimates, also known as ability estimates, were then correlated with the raw score estimates calculated from the sum of all the items responses. Concurrent validity, which claims a measurement is valid if the correlation between the new measurement and the valid measurement is greater or equal to .90, was used to determine the validity of IRT methodology for the WLQ. Ability estimates from IRT were found to be somewhat highly correlated with the raw scores from the WLQ (above .80). However, the only subscale which had a high enough correlation for IRT to be considered valid was the time management subscale (r = .90). All other subscales, mental/interpersonal, physical, and output, did not produce valid IRT ability estimates.^ An explanation for these lower than expected correlations can be explained by the outliers found in the sample. Also, acquiescent responding (AR) bias, which is caused by the tendency for people to respond the same way to every question on a questionnaire, and the multidimensionality of the questionnaire (the WLQ is composed of four dimensions and thus four different latent variables) probably had a major impact on the IRT estimates. Furthermore, it is possible that the mental/interpersonal dimension violated the monotonocity assumption of IRT causing PARSCALE to fail to run for these estimates. The monotonicity assumption needs to be checked for the mental/interpersonal dimension. Furthermore, the use of multidimensional IRT methods would most likely remove the AR bias and increase the validity of using IRT to analyze data from the WLQ.^

Mathematics, computers in Mathematics, and gender : public perceptions in context. 'Matemáticas, ordenadores en Matemáticas y género : percepciones públicas en contexto'.

Resumen basado en el de la publicación

Modeling a health promotion campaign process

In this work we will present a model that describes how the number of healthy and unhealthy subjects that belong to a cohort, changes through time when there are occurrences of health promotion campaigns aiming to change the undesirable behavior. This model also includes immigration and emigration components for each group and a component taking into account when a subject that used to perform a healthy behavior changes to perform the unhealthy behavior. We will express the model in terms of a bivariate probability generating function and in addition we will simulate the model. ^ An illustrative example on how to apply the model to the promotion of condom use among adolescents will be created and we will use it to compare the results obtained from the simulations and the results obtained by the probability generating function. ^

Trans-disciplinary research in synthesis of grass pollen aerobiology and its importance for respiratory health in Australasia

Grass pollen is a major trigger for allergic rhinitis and asthma, yet little is known about the timing and levels of human exposure to airborne grass pollen across Australasian urban environments. The relationships between environmental aeroallergen exposure and allergic respiratory disease bridge the fields of ecology, aerobiology, geospatial science and public health. The Australian Aerobiology Working Group comprised of experts in botany, palynology, biogeography, climate change science, plant genetics, biostatistics, ecology, pollen allergy, public and environmental health, and medicine, was established to systematically source, collate and analyse atmospheric pollen concentration data from 11 Australian and six New Zealand sites. Following two week-long workshops, post-workshop evaluations were conducted to reflect upon the utility of this analysis and synthesis approach to address complex multidisciplinary questions. This Working Group described i) a biogeographically dependent variation in airborne pollen diversity, ii) a latitudinal gradient in the timing, duration and number of peaks of the grass pollen season, and iii) the emergence of new methodologies based on trans-disciplinary synthesis of aerobiology and remote sensing data. Challenges included resolving methodological variations between pollen monitoring sites and temporal variations in pollen datasets. Other challenges included “marrying” ecosystem and health sciences and reconciling divergent expert opinion. The Australian Aerobiology Working Group facilitated knowledge transfer between diverse scientific disciplines, mentored students and early career scientists, and provided an uninterrupted collaborative opportunity to focus on a unifying problem globally. The Working Group provided a platform to optimise the value of large existing ecological datasets that have importance for human respiratory health and ecosystems research. Compilation of current knowledge of Australasian pollen aerobiology is a critical first step towards the management of exposure to pollen in patients with allergic disease and provides a basis from which the future impacts of climate change on pollen distribution can be assessed and monitored.

“All work and no play … ?” : a critical investigation of an emerging public health discourse on children’s play.

Dans de nombreuses sociétés industrialisées, une grande valeur est attribuée au jeu des enfants, principalement parce que le jeu est considéré comme étant une composante essentielle de leur développement et qu’il contribue à leur bonheur et à leur bien-être. Toutefois, des inquiétudes ont récemment été exprimées au regard des transformations qui s’opèrent dans le jeu des enfants, notamment en ce qui a trait à la réduction du temps de jeu en plein air. Ces transformations ont été attribuées, en grande partie, à une perception de risques accrus associés au jeu en plein air et à des changements sociaux qui favorisent des activités de loisirs plus structurées et organisées. L’inquiétude concernant la diminution de l’espace-temps accordé au jeu des enfants est d’ailleurs clairement exprimée dans le discours de la santé publique qui, de plus, témoigne d’un redoublement de préoccupations vis-à-vis du mode de vie sédentaire des enfants et d’une volonté affirmée de prévention de l'obésité infantile. Ainsi, les organisations de santé publique sont désormais engagées dans la promotion du jeu actif pour accroître l'activité physique des enfants. Nous assistons à l’émergence d’un discours de santé publique portant sur le jeu des enfants. À travers quatre articles, cette thèse explore le discours émergeant en santé publique sur le jeu des enfants et analyse certains de ses effets potentiels. L'article 1 présente une prise de position sur le sujet du jeu en santé publique. J’y définis le cadre d'analyse de cette thèse en présentant l'argument central de la recherche, les positions que les organisations de santé publique adoptent vis-à-vis le jeu des enfants et les répercussions potentielles que ces positions peuvent avoir sur les enfants et leurs jeux. La thèse permet ensuite d’examiner comment la notion de jeu est abordée par le discours de santé publique. L'article 2 présente ainsi une analyse de discours de santé publique à travers 150 documents portant sur la santé, l'activité physique, l'obésité, les loisirs et le jeu des enfants. Cette étude considère les valeurs et les postulats qui sous-tendent la promotion du jeu comme moyen d’améliorer la santé physique des enfants et permet de discerner comment le jeu est façonné, discipliné et normalisé dans le discours de santé publique. Notre propos révèle que le discours de santé publique représente le jeu des enfants comme une activité pouvant améliorer leur santé; que le plaisir sert de véhicule à la promotion de l’activité physique ; et que les enfants seraient encouragés à organiser leur temps libre de manière à optimiser leur santé. Étant donné l’influence potentielle du discours de santé publique sur la signification et l’expérience vécue du jeu parmi les enfants, cette thèse présente ensuite une analyse des représentations qu’ont 25 enfants âgés de 7 à 11 ans au regard du jeu. L’article 3 suggère que le jeu est une fin en soi pour les enfants de cette étude; qu'il revêt une importance au niveau émotionnel; et qu'il s’avère intrinsèquement motivé, sans but particulier. De plus, l’amusement que procure le jeu relève autant d’activités engagées que d’activités sédentaires. Enfin, certains enfants expriment un sentiment d'ambivalence concernant les jeux organisés; tandis que d’autres considèrent parfois le risque comme une composante particulièrement agréable du jeu. De tels résultats signalent une dissonance entre les formes de jeux promues en santé publique et le sens attribué au jeu par les enfants. Prenant appui sur le concept de « biopédagogies » inspiré des écrits de Michel Foucault, le quatrième article de cette thèse propose un croisement des deux volets de cette étude, soit le discours de santé publique sur le jeu et les constructions du jeu par les enfants. Bien que le discours de la santé publique exhortant au «jeu actif» soit reproduit par certains enfants, d'autres soulignent que le jeu sédentaire est important pour leur bien-être social et affectif. D’autre part, tandis que le « jeu actif » apparait, dans le discours de santé publique, comme une solution permettant de limiter le risque d'obésité, il comporte néanmoins des contradictions concernant la notion de risque, dans la mesure où les enfants ont à négocier avec les risques inhérents à l’activité accrue. À terme, cet article suggère que le discours de santé publique met de l’avant certaines représentations du jeu (actifs) tandis qu’il en néglige d’autres (sédentaires). Cette situation pourrait donner lieu à des conséquences inattendues, dans la mesure où les enfants pourraient éventuellement reconfigurer leurs pratiques de jeu et les significations qu’ils y accordent. Cette thèse n'a pas pour but de fournir des recommandations particulières pour la santé publique au regard du jeu des enfants. Prenant appui sur la perspective théorique de Michel Foucault, nous présentons plutôt une analyse d’un discours émergeant en santé publique ainsi que des pistes pour la poursuite de recherches sur le jeu dans le domaine de l’enfance. Enfin, compte tenu des effets potentiels du discours de la santé publique sur le jeu des enfants, et les perspectives contemporaines sur le jeu et les enfants, la conclusion offre des pistes de réflexion critique.