STATISTICAL METHODS FOR RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES IN COMPLEX SAMPLING SURVEYS

Sample surveys are critical in providing information in a broad range of areas, serving as a valuable resource for guiding actions and policies. Classical methods in inferential statistics assume that the observations were selected according to a simple random sampling from a population of interest. However, in large-scale surveys, the final sample usually does not represent a simple random sample of independent, identically distributed observations from an infinite population. Instead, these studies use complex survey designs, including stratification, multistage cluster sampling, and unequal selection probabilities to obtain a representative sample more efficiently in terms of time and cost. Failure to account for the complex survey design may result in biased parameter estimators, underestimated standard errors, and possibly misleading conclusions.The receiver operating characteristic (ROC) curve is the most popular tool used to evaluate the accuracy of diagnostic tests measured on a continuous scale. Currently, analyses based on the ROC curve have been performed on data arising from complex survey samples ignoring the sampling scheme. For our first topic, we propose a nonparametric estimator for the ROC curve that accounts for complex survey sampling and establish its uniform convergence. The properties of the estimator are evaluated through simulation studies and illustrated using the National Health and Nutrition Examination Survey(NHANES).Nonresponse is a common issue in surveys and can induce bias if not adequately accounted for. For our second topic, we propose an IPW estimator for the ROC curve to accommodate the case where the diagnostic test is missing. The theoretical properties of the estimator are developed and evaluated using simulation studies. The proposed estimator is then applied to the NHANES data.In many applications, it is desired to study the covariate effects on the accuracy of a diagnostic test. In our third topic, we adapt a popular model referred to as ROC-GLM, proposed by Pepe (2000a) and Alonzo and Pepe (2002) to account for complex survey designs. Simulation studies show that our design-adjusted ROC-GLM model performs well compared to the original model, which was developed for simple random sampling. To illustrate our method, we study the effect of age on the accuracy of a diabetes assessment calculator.Doctor of Philosoph

Corpora amylacea are associated with tau burden and cognitive status in Alzheimer\u27s disease

Corpora amylacea (CA) and their murine analogs, periodic acid Schiff (PAS) granules, are age-related, carbohydrate-rich structures that serve as waste repositories for aggregated proteins, damaged cellular organelles, and other cellular debris. The structure, morphology, and suspected functions of CA in the brain imply disease relevance. Despite this, the link between CA and age-related neurodegenerative diseases, particularly Alzheimer\u27s disease (AD), remains poorly defined. We performed a neuropathological analysis of mouse PAS granules and human CA and correlated these findings with AD progression. Increased PAS granule density was observed in symptomatic tau transgenic mice and APOE knock-in mice. Using a cohort of postmortem AD brain samples, we examined CA in cognitively normal and dementia patients across Braak stages with varying APOE status. We identified a Braak-stage dependent bimodal distribution of CA in the dentate gyrus, with CA accumulating and peaking by Braak stages II-III, then steadily declining with increasing tau burden. Refined analysis revealed an association of CA levels with both cognition and APOE status. Finally, tau was detected in whole CA present in human patient cerebrospinal fluid, highlighting CA-tau as a plausible prodromal AD biomarker. Our study connects hallmarks of the aging brain with the emergence of AD pathology and suggests that CA may act as a compensatory factor that becomes depleted with advancing tau burden

Additive-multiplicative hazard models in survival analysis

Este trabalho visa estudar o modelo de Cox-Aalen, que incorpora efeitos fixos e variantes no tempo por meio das estruturas aditiva e multiplicativa. Os principais modelos aditivos e multiplicativos que englobam efeitos fixos e variantes no tempo são revisados com enfoque de processos de contagem e um teste do tipo escore para auxiliar na escolha entre a estrutura aditiva e multiplicativa é proposto e avaliado por meio de estudos de simulação. Aplicação e comparação dos principais modelos discutidos em dados do Instituto do Câncer do Estado de São Paulo (ICESP) sugerem que o modelo estudado pode ser uma opção interessante na análise de dados de sobrevivência em que as ferramentas usuais não são adequadas.This dissertation aims to study the Cox-Aalen regression model, which incorporates fixed and time-varying effects through the additive and multiplicative structures. The primary additive and multiplicative risk models that deal with fixed and time-dependent effects are reviewed with the counting process framework and a score test to assist the decision between the additive and multiplicative structures is proposed and evaluated with a simulation study. The application of the models to the Instituto do Câncer do Estado de São Paulo (ICESP) dataset suggests that the model under study can be considered as an interesting option to analyse survival data when the usual techniques are not aproppriate