A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^

A simulation study of the effects of small center enrollment in multi -center clinical trials

Multi-center clinical trials are very common in the development of new drugs and devices. One concern in such trials, is the effect of individual investigational sites enrolling small numbers of patients on the overall result. Can the presence of small centers cause an ineffective treatment to appear effective when treatment-by-center interaction is not statistically significant?^ In this research, simulations are used to study the effect that centers enrolling few patients may have on the analysis of clinical trial data. A multi-center clinical trial with 20 sites is simulated to investigate the effect of a new treatment in comparison to a placebo treatment. Twelve of these 20 investigational sites are considered small, each enrolling less than four patients per treatment group. Three clinical trials are simulated with sample sizes of 100, 170 and 300. The simulated data is generated with various characteristics, one in which treatment should be considered effective and another where treatment is not effective. Qualitative interactions are also produced within the small sites to further investigate the effect of small centers under various conditions.^ Standard analysis of variance methods and the "sometimes-pool" testing procedure are applied to the simulated data. One model investigates treatment and center effect and treatment-by-center interaction. Another model investigates treatment effect alone. These analyses are used to determine the power to detect treatment-by-center interactions, and the probability of type I error.^ We find it is difficult to detect treatment-by-center interactions when only a few investigational sites enrolling a limited number of patients participate in the interaction. However, we find no increased risk of type I error in these situations. In a pooled analysis, when the treatment is not effective, the probability of finding a significant treatment effect in the absence of significant treatment-by-center interaction is well within standard limits of type I error. ^

Development and testing of a procedure for systematically assigning gestational age for clinical and research use

A graphing method was developed and tested to estimate gestational ages pre-and postnatally in a consistent manner for epidemiological research and clinical purposes on feti/infants of women with few consistent prenatal estimators of gestational age. Each patient's available data was plotted on a single page graph to give a comprehensive overview of that patient. A hierarchical classification of gestational age determination was then applied in a systematic manner, and reasonable gestational age estimates were produced. The method was tested for validity and reliability on 50 women who had known dates for their last menstrual period or dates of conception, and multiple ultrasound examinations and other gestational age estimating measures. The feasibility of the procedure was then tested on 1223 low income women with few gestational age estimators. The graphing method proved to have high inter- and intrarater reliability. It was quick, easy to use, inexpensive, and did not require special equipment. The graphing method estimate of gestational age for each infant was tested against the last menstrual period gestational age estimate using paired t-Tests, F tests and the Kolmogorov-Smirnov test of similar populations, producing a 98 percent probability or better that the means and data populations were the same. Less than 5 percent of the infants' gestational ages were misclassified using the graphing method, much lower than the amount of misclassification produced by ultrasound or neonatal examination estimates. ^

A standard operation procedure and a pilot water testing for herbicides in northwest Harris County, Texas

Herbicides are used to control the growth of weeds along highways, power lines, and many other urban locations. Exposure to herbicides has been linked to adverse health outcomes. This study was initiated to pretest for the presence of herbicides in multiple water sources near intersections in a corridor in the Northwest Harris County (specifically in the Highway 6/FM 1960, North Freeway 45, US 290 and S 99 corridor). Roadside water and tap water samples were collected and analyzed for herbicides using the established Environmental Protection Agency (EPA) Method 515.4: "Determination of Chlorinated Acids in Drinking Water by Liquid-Liquid Micro-extraction, Derivatization, and Fast Gas Chromatography with Electron Capture Detection." A standard operating procedure (adapted from the US EPA Method 515.4) was developed for subsequent, larger studies of environmental fate of herbicides and non-occupational exposure risks. Preliminary testing of 16 water samples was performed to pretest the existence of trace herbicides; all concentrations that were greater than the minimum reporting limits of each analyte are reported with a 99 percent confidence. This study failed to find concentrations above the limits of detection of the method in any of the samples collected on June 15, 2008. However, this does not indicate that the waters around the NW Harris County are free of herbicides and metabolites. A larger and repeated sampling in the region would be necessary to make that claim. ^

ATTITUDES TOWARD AND UTILIZATION OF NON-INVASIVE PRENATAL TESTING FOR CHROMOSOME ANEUPLOIDY AMONG OB/GYNS

Prenatal diagnosis is traditionally made via invasive procedures such as amniocentesis and chorionic villus sampling (CVS). However, both procedures carry a risk of complications, including miscarriage. Many groups have spent years searching for a way to diagnose a chromosome aneuploidy without putting the fetus or the mother at risk for complications. Non-invasive prenatal testing (NIPT) for chromosome aneuploidy became commercially available in the fall of 2011, with detection rates similar to those of invasive procedures for the common autosomal aneuploidies (Palomaki et al., 2011; Ashoor et al. 2012; Bianchi et al. 2012). Eventually NIPT may become the diagnostic standard of care and reduce invasive procedure-related losses (Palomaki et al., 2011). The integration of NIPT into clinical practice has potential to revolutionize prenatal diagnosis; however, it also raises some crucial issues for practitioners. Now that the test is clinically available, no studies have looked at the physicians that will be ordering the testing or referring patients to practitioners who do. This study aimed to evaluate the attitudes of OB/GYN’s and how they are incorporating the test into clinical practice. Our study shows that most physicians are offering this new, non-invasive technology to their patients, and that their practices were congruent with the literature and available professional society opinions. Those physicians who do not offer NIPT to their patients would like more literature on the topic as well as instructive guidelines from their professional societies. Additionally, this study shows that the practices and attitudes of MFMs and OBs differ. Our population feels that the incorporation of NIPT will change their practices by lowering the amount of invasive procedures, possibly replacing maternal serum screening, and that it will simplify prenatal diagnosis. However, those physicians who do not offer NIPT to their patients are not quite sure how the test will affect their clinical practice. From this study we are able to glean how physicians are incorporating this new technology into their practice and how they feel about the addition to their repertoire of tests. This knowledge gives insight as to how to best move forward with the quickly changing field of prenatal diagnosis.