Methods needed to measure predictive accuracy: A study of diabetic patients

Diabetes is one of the leading causes of morbidity and mortality and it can result in several complications such as kidney failure, heart failure, stroke, and blindness making it a major medical and public health concern in the United States. Statistical methods are important to detect risk factors and identify the best sampling plan to determine predictive bounds for diabetic patients\u27 data. The main objective of this paper is to identify the best fit bootstrapping sampling method and to draw the predictive bound considering diabetes patient data. A random sample was used from the National Health and Nutritional Examination Survey (NHANES) for this study. We found that there were significant relationships between age, marital status, and race/ethnicity with diabetes status (p \u3c 0.001) and no relationship was observed between gender and diabetes status. We ran the logistic regression to identify the risk factors from the data. We identified that the significant risk factors are age (p \u3c 0.001), total protein (p \u3c 0.001), fast food (p \u3c 0.0339), and direct HDL (p \u3c 0.001). This study provides evidence that the parametric bootstrapping method is the best fit method compared with other methods to estimate the predictive error bounds. These findings will be of great significance for identifying the best sampling methods, which can increase the statistical accuracy of laboratory clinical research of diabetes. This will also allow for the determination of precise risk factors that will best represent the data by detecting mild and extreme outliers from disease observations. Therefore, these results will be useful for researchers and clinicians to select the best sampling methods to study diabetes and other diseases in order to maximize the accuracy of their results. This article is part of a Special Issue entitled: Oxidative Stress and Mitochondrial Quality in Diabetes/Obesity and Critical Illness Spectrum of Diseases - edited by P. Hemachandra Reddy

Sex Differences in Cardiovascular Disease and Cognitive Dysfunction in Rural West Elderly Texans

BACKGROUND: The prevalence of cognitive dysfunction increases in elderly due to cardiovascular disease related risk factors in rural communities like West Texas. OBJECTIVE: The purpose of this study was to find risk factors of cardiovascular disease (CVD) related to cognitive dysfunction and their impact on elderly adults in rural West Texans. METHODS: Statistical methods such as Pearson\u27s chi-squared and a multinomial logistic regression were utilized to analyze data. We used SPSS software to detect and understand the nature of the risk factors. RESULTS: A summary of statistics was obtained by using Pearson\u27s chi-squared test for categorical variables. CVD, diabetes mellitus, and depression were significantly associated with cognitive dysfunction for both males and females ( = 0.0001), whereas anxiety was found to be significantly associated with cognitive dysfunction for females ( = 0.0001). Age group and race/ethnicity were significantly associated with cognitive dysfunction for both males and females ( = 0.0001). By performing a multinomial logistic regression method and controlling for confounders, the significant risk factors ( \u3c  0.05)- age (65- 84 years), diabetes, and memory loss for age-associated cognitive impairment; diabetes for cognitive impairment no dementia; age (65- 84, ≥85 years), CVD, diabetes, depression, memory loss, non-Hispanic Whites, and Black/African-Americans for mild cognitive impairment; and age, memory loss, non-Hispanic Whites, Black/African-Americans, and male gender were found for dementia. CONCLUSION: CVD related risk factors in developing cognitive dysfunction exist and integrating such risk variables may guide relevant policy interventions to reduce Alzheimer\u27s incidence or dementia in rural communities in West Texans

Does Survival Vary for Breast Cancer Patients in the United States? A Study from Six Randomly Selected States

Background . Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined. Materials and Methods . A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states. Results . Iowa had the highest mean age of diagnosis at 64.14 years ( S E = 0.324 ) and Georgia had the lowest at 57.97 years ( S E = 0.313 ). New Mexico had the longest mean survival time of 189.09 months ( S E = 20.414 ) and Hawaii the shortest at 119.01 ( S E = 5.394 ) months, a 70.08-month difference (5.84 years). Analysis of stage of diagnosis showed that the highest survival times for Whites and American Indians/Alaska Natives were for stage I cancers. The highest survival times for Blacks varied. Stage IV cancer consistently showed the lowest survival times. Conclusions . Differences in breast cancer characteristics across states highlight the need to understand differences between the states that result in variances in breast cancer survival

Does breast cancer drive the building of survival probability models among States? An assessment of goodness of fit for patient data from SEER registries

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer