Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

A meta -analysis of empirical research studies on resilience among students at -risk for school failure

Being at-risk is a growing problem in the U.S. because of disturbing societal trends such as unemployment, divorce, substance abuse, child abuse and neglect, and the new threat of terrorist violence. Resilience characterizes individuals who rebound from or adapt to adversities such as these, and academic resilience distinguishes at-risk students who succeed in school despite hardships. ^ The purpose of this research was to perform a meta-analysis to examine the power of resilience and to suggest ways educators might improve academic resilience, which was operationalized by satisfactory test scores and grades. In order to find all studies that were relevant to academic resilience in at-risk kindergarten through 12th-grade students, extensive electronic and hardcopy searches were conducted, and these resulted in a database of 421 articles. Two hundred eighty seven of these were rejected quickly, because they were not empirical research. Upon further examination, another 106 were rejected for not meeting study protocol criteria. Ultimately, 28 studies were coded for study level descriptors and effect size variables. ^ Protective factors for resilience were found to originate in physical, psychological, and behavioral domains on proximal/intraindividual, transitional/intrafamilial, or distal/extrafamilial levels. Effect sizes (ESs) for these were weighted and the means for each level or category were interpreted by commonly accepted benchmarks. Mean effect sizes for proximal (M = .27) and for transitional (M = .15) were small but significant. The mean effect size for the distal level was insignificant. This supported the hypotheses that the proximal level was the source of most protective factors for academic resilience in at-risk students followed by the transitional level. The distal effect size warranted further research particularly in light of the small number of studies (n = 11) contributing effect sizes to that category. A homogeneity test indicated a search for moderators, i.e., study variables affecting outcomes, was justified. “Category” was the largest moderator. Graphs of weighted mean effect sizes in the physical, psychological, and behavioral domains were plotted for each level to better illustrate the findings of the meta-analysis. Suggestions were made for combining resilience development with aspects of positive psychology to promote resilience in the schools. ^

The relationship between cognitive ability, the big five, task and contextual performance: a meta-analysis

For years, researchers and human resources specialists have been searching for predictors of performance as well as for relevant performance dimensions (Barrick & Mount, 1991; Borman & Motowidlo, 1993; Campbell, 1990; Viswesvaran et al., 1996). In 1993, Borman and Motowidlo provided a framework by which traditional predictors such as cognitive ability and the Big Five personality factors predicted two different facets of performance: 1) task performance and 2) contextual performance. A meta-analysis was conducted to assess the validity of this model as well as that of other modified models. The relationships between predictors such as cognitive ability and personality variables and the two outcome variables were assessed. It was determined that even though the two facets of performance may be conceptually different, empirically they overlapped substantially (p= .75). Finally, results show that there is some evidence for cognitive ability as a predictor of both task and contextual performance and conscientiousness as a predictor of both task and contextual performance. The possible mediation of predictor-- criterion relationships was also assessed. The relationship between cognitive ability and contextual performance vanished when task performance was controlled.

Elder malnutrition: A meta analysis of studies using the nutrition screening initiative determine checklist

To demonstrate the extent of elder malnutrition risk in this country, a meta analysis of 30 studies that used the Nutrition Screening Initiative's "DETERMINE Your Nutritional Health Checklist" was conducted. The studies were divided into three categories: Elder Populations in Healthcare Related Systems (HS), Elder Meal Program Participants (MP), and General Community Residing Elder Population (GC). HS was sub-divided into Hospital Inpatient/Clinic Outpatient and Long Term Care/Home Care. The MP population were identified as Congregate or Home Delivered meal participants. Overall, results indicate that 63% of elders are at moderate (32%) or high (31 %) risk of malnutrition. Home Delivered Meal Participants and Hospital Inpatient/Clinic Outpatients are most likely to be at high risk of malnutrition (49.5% and 49.1%, respectively). Individuals least likely to be at high malnutrition risk are the General Community Residing Elder Population (17.5%) and Congregate Meal Participants (22.5%).

The State of our Toolbox: A Meta-analysis of Reliability Measurement Precision

My study investigated internal consistency estimates of psychometric surveys as an operationalization of the state of measurement precision of constructs in industrial and organizational (I/O) psychology. Analyses were conducted of samples used in research articles published in the Journal of Applied Psychology between 1975 and 2010 in five year intervals (K = 934) from 480 articles yielding 1427 coefficients. Articles and their respective samples were coded for test-taker characteristics (e.g., age, gender, and ethnicity), research settings (e.g., lab and field studies), and actual tests (e.g., number of items and scale anchor points). A reliability and inter-item correlations depository was developed for I/O variables and construct groups. Personality measures had significantly lower inter-item correlations than other construct groups. Also, internal consistency estimates and reporting practices were evaluated over time, demonstrating an improvement in measurement precision and missing data.