Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>This article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F_2α(EPI8), 24 h urine 11-dehydro-thromboxane B₂(DEH11), and high-density lipoprotein cholesterol (HDL).</p> <p>Methods</p> <p>Random Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models</p> <p>Results</p> <p>The analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39).</p> <p>Conclusions</p> <p>Levels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors togather explain 29%-41% of the variability in the BOPH.</p

District Fiscal Policy and Student Achievement

School restructuring raises questions about the role of school districts in improving student learning. Centralization by state governments and decentralization to individual schools as proposed in systemic reform leave districts' role unsettled. Empirical research on the district role in the context of ongoing reform is inadequate. This analysis of combined data from the NAEP and the Common Core of Data (CCD) was intended to address the issue. We analyzed 1990, 1992, and 1996 NAEP 8th grade mathematics national assessment data in combination with CCD data of corresponding years to examine the extent to which student achievement was related to districts' control over instructional expenditure, adjusting for relevant key factors at both district and student levels. Upon sample modification, we used hierarchical linear modeling (HLM) to estimate the relationships of student achievement to two district fiscal policy indictors, current expenditure per pupil (CEPP) and districts' discretionary rates for instructional expenditure (DDR). Net of relevant district factors, DDR was found unrelated to districts' average 8th grade math performance. The null effect was consistent in the analysis of the combined NAEP-CCD data for 1990, 1992, and 1996. In contrast, CEPP was found related to higher math performance in a modest yet fairly consistent way. Future research may be productive to separately study individual states and integrate the findings onto the national level

Multiple imputation for estimating the risk of developing dementia and its impact on survival

Dementia, Alzheimer\u27s disease in particular, is one of the major causes of disability and decreased quality of life among the elderly and a leading obstacle to successful aging. Given the profound impact on public health, much research has focused on the age-specific risk of developing dementia and the impact on survival. Early work has discussed various methods of estimating age-specific incidence of dementia, among which the illness-death model is popular for modeling disease progression. In this article we use multiple imputation to fit multi-state models for survival data with interval censoring and left truncation. This approach allows semi-Markov models in which survival after dementia depends on onset age. Such models can be used to estimate the cumulative risk of developing dementia in the presence of the competing risk of dementia-free death. Simulations are carried out to examine the performance of the proposed method. Data from the Honolulu Asia Aging Study are analyzed to estimate the age-specific and cumulative risks of dementia and to examine the effect of major risk factors on dementia onset and death

Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data

According to the American Cancer Society report (1999), cancer surpasses heart disease as the leading cause of death in the United States of America (USA) for people of age less than 85. Thus, medical research in cancer is an important public health interest. Understanding how medical improvements are affecting cancer incidence, mortality and survival is critical for effective cancer control. In this paper, we study the cancer survival trend on the population level cancer data. In particular, we develop a parametric Bayesian joinpoint regression model based on a Poisson distribution for the relative survival. To avoid identifying the cause of death, we only conduct analysis based on the relative survival. The method is further extended to the semiparametric Bayesian joinpoint regression models wherein the parametric distributional assumptions of the joinpoint regression models are relaxed by modeling the distribution of regression slopes using Dirichlet process mixtures. We also consider the effect of adding covariates of interest in the joinpoint model. Three model selection criteria, namely, the conditional predictive ordinate (CPO), the expected predictive deviance (EPD), and the deviance information criteria (DIC), are used to select the number of joinpoints. We analyze the grouped survival data for distant testicular cancer from the Surveillance, Epidemiology, and End Results (SEER) Program using these Bayesian models.

General Framework for Equivalence Testing over a Range of Linear Outcomes with CMC Applications

<p>As per regulatory guidance, it is mandatory to demonstrate comparability before and after a change is made to an analytical method for commercial lot release or manufacturing process in the area of Chemistry, Manufacturing, and Controls. The change may include transfer assay or manufacturing process to a different location. Use of statistical methods to assess comparability before and after the change across the range of interest is a regulatory requirement. Regardless of the types of the changes, comparability is often demonstrated using analytical data collected pre- and post-change. For instance, assay transfer requires the demonstration of comparable assay performance over a range of expected responses. Although there are various methods used in practice or proposed in the published literature, there is no consensus on the best practice. For measurements that exhibit linearity in the outcome variable, a general statistical framework of equivalence testing is proposed. The equivalence test can be carried out either through Bayesian or frequentist analyses.  The method is illustrated via several real-world examples.</p