Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

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. ^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^

Medios de comunicación y democracia : apuntes para el análisis de una relación compleja

Muchas de las transformaciones de la política y del funcionamiento democrático son atribuidas a un proceso creciente, en los últimos años, de massmediatización. Estas transformaciones son evaluadas negativamente en dos sentidos: comparativamente, en relación a un estadio de la democracia con ciudadanos plenamente involucrados y participativos, en el cual la escena política no estaba dominada por la TV; y en forma culpabilizadora, acusando a los medios de comunicación de engullir el mundo, en consecuencia, acusando a la TV de engullir a la política. En el presente artículo se analiza el fenómeno descripto situándolo en el contexto de algunas transformaciones culturales, económicas y antropológicas a fin de superar la condena fácil y mecánica, producto de una mirada centrada en la omnipotencia de los medios.

Aspectos odontológicos importantes en la atención de pacientes con Síndrome de Prader Willi

El Síndrome de Prader Willi es una enfermedad presente al nacer que fue descrita en 1956 por los doctores suizos Prader, Labarth y Willi, que involucra obesidad, disminución del tono muscular, trastornos cognitivos y que representa un reto para el profesional odontólogo cuando aparecen trastornos de conducta importantes. El tratamiento del paciente con este síndrome requiere una aproximación multidisciplinar para su cuidado. Aunque el síndrome se diagnostica a menudo en el periodo neonatal, puede no ser sospechado hasta la aparición de la obesidad unos años más tarde en la infancia. La incidencia y frecuencia publicada es muy variable, aceptándose que 1 de cada 15.000 niños nace con esta compleja alteración genética. La combinación de problemas nutricionales, médicos y de conducta es un desafío que debe afrontarse en un equipo de profesionales en el que el Odontólogo compartirá saberes para mejorar la salud integral de estos pacientes.

La compleja construcción de la problemática de la seguridad : violencia y políticas públicas de seguridad a comienzos del siglo XXI

Los primeros diez años del nuevo siglo nos encuentran con un bagaje de producción científica y trabajos de campo a partir de los cuales podemos reflexionar sobre la importancia de abordar la violencia cuando hablamos de políticas públicas de seguridad en sociedades democráticas. Ello implica reconocer un recorte del tema y una toma de posición alrededor de la cual iremos reconstruyendo la problemática de la seguridad que va más allá del delincuente, la asociación del problema de la seguridad al problema del delito, la tipificación de delitos y las técnicas disciplinarias. Nuestra atención esta dirigida a la violencia, la política expresada en políticas públicas y la expresión de las subjetividades en el miedo a ser víctima.

"Nos gritaron que nos fuéramos" (¿o que nos vayamos?) : El uso variable de los tiempos verbales como estrategia etnopragmática

Nuestro objetivo es analizar el uso alternante del Presente y el Pretérito Imperfecto del Modo Subjuntivo en un corpus constituido por emisiones pertenecientes a artículos periodísticos. El problema propuesto está vinculado a la evidencialidad como sustancia semántica que subyace a la selección de las formas. Esto es, en este caso, la posibilidad de determinar la evaluación y el grado de compromiso que cada sujeto establece con la fuente de información y la evaluación que hace de esta última. Algunas lenguas poseen morfemas específicos con los que se indica dicha función, otras, en cambio, no disponen de tales morfemas -tal es el caso del español- razón por la cual sus usuarios echan mano a ciertos recursos que la lengua les provee. Desde el enfoque de la Etnopragmática, intentaremos explicar en qué consisten los usos alternantes que los sujetos realizan en la conformación de enunciados en los que los verbos principal y dependiente se hallan en correlación temporal, descubrir a qué factores responde la selección de los verbos dependientes y relacionar tales factores con la motivación que da lugar a la selección de las formas en variación.

Aspectos en el uso variable del perfecto compuesto de San Juan : Inclusión y persuasión en discursos políticos

El contacto con la variedad del español utilizada por hablantes de la Provincia de San Juan permite observar usos alternantes de los tiempos pasados Perfecto Simple y Perfecto Compuesto con mayor frecuencia de uso de éste con respecto a la zona rioplatense. El compuesto se emplea -por los sanjuaninos- para señalar aspectos semánticos reservados para el Simple como el referir acciones acabadas lejanas al momento de la enunciación o incluso sin ninguna relación con el mismo. El hablante puede comunicar factores subjetivos con el Perfecto Compuesto usándolo como una estrategia discursiva cargada de subjetividad que le permite hacer relevantes los hechos pasados, sin importar si están más o menos alejados del momento de la enunciación, y produciendo con ello una ampliación semántica en la idea de pasado que el compuesto comunica. El presente trabajo -en el marco de los principios de la Escuela de Columbia (Diver, 1995) y la Etnopragmática (García, 1995; Martínez, 1995, 2000; Mauder, 2000)- continúa con el análisis de aspectos esbozados en otro anterior (Gentili, 2011), también sobre corpus de discurso político, considerado este como práctica social que funciona no sólo como instrumento con fines políticos particulares, sino como creador y sostén de maneras de pensar, hablar y actuar: o sea, formas de vida y visiones de mundo. Para ello, el enunciador debe poner al servicio del objetivo comunicativo el uso de una gramática compartida situándose temporalmente para ejercer el poder y lograr la adhesión mediante la persuasión. Sospechamos, entonces, que el perfecto compuesto está asociado con la idea de incluir al oyente a una forma particular de perspectiva sobre del pasado referido, y de persuadirlo no solo de esa visión de los hechos pasados sino lograr también la adhesión al compromiso veredictivo que, como enunciador político, quiere trasmitir. Los resultados contribuirán a la perspectiva teórica que contempla la motivación semántico-pragmática de la gramática y su relación con el uso de la lengua