Depression is the most common psychiatric disorder and the leading cause of disability worldwide. Despite evidence for a genetic component, the genetic aetiology of this disorder remains elusive. To date, only one association study has identified and replicated risk loci for depression. This thesis focuses on aiding genetic discovery by revisiting the depressed phenotype and developing a quantitative trait, using data from Generation Scotland: The Scottish Family Health Study. These analyses aim to test whether this derived quantitative trait has improved statistical power to identify genetic risk variants for depression, relative to the binary classification of case/control. Measures of genetic covariation were used to evaluate and rank ten measures of mood, personality and cognitive ability as endophenotypes for depression. The highest ranking traits were subjected to principal component analysis, and the first principal component used as a quantitative measure of depression. This composite trait was compared to the binary classification of depression in terms of ability to identify risk loci in a genome-wide association study, and phenotypic variance explained by polygenic profile scores for psychiatric disorders. I also compared the composite trait to the univariate traits in terms of their ability to fulfill the endophenotype criteria as described by Gottesman and Gould, namely: being heritable, genetically and phenotypically correlated with depression, state independent, co-segregating with illness in families, and observed at a higher rate in unaffected relatives than in unrelated controls. Four out of ten traits fulfilled most endophenotype criteria, however, only two traits - neuroticism and the general health questionnaire (a measure of current psychological distress) - consistently ranked highest across all analyses. As such, three composite traits were derived incorporating two, three, or four traits. Association analyses of binary depression, univariate traits and composite traits yielded no genome-wide significant results, with most traits performing equivalently. However, composite traits were more heritable and more highly correlated with depression than their constituent traits, suggesting that analyzing these traits in combination was capturing more of the heritable component of depression. Polygenic scores for psychiatric disorders explained more trait variance for the composite traits than the univariate traits, and depression itself. Overall, whilst the composite traits generally obtained more significant results, they did not identify any further insight into the genetic aetiology of depression. This work therefore provides support for the urgent need to redefine the depressed phenotype based on objective and quantitative measures. This is essential for risk stratification, better diagnoses, novel target identification and improved treatment.

As awareness of the costs of workplace injury and illness continues to grow, there has been an increased demand for effective ergonomic interventions to reduce the prevalence of musculoskeletal disorders (MSDs). The goal of ergonomic interventions is to reduce exposures (mechanical and psychosocial); however there is conflicting evidence about the impact of these interventions as many studies produce inconclusive or conflicting results. In order to provide a clearer picture of the effectiveness of these interventions, we must find out if methodological issues, particularly statistical power, are limiting this research. The purpose of this study was to review and examine factors influencing statistical power in ergonomic intervention papers from five peer reviewed journals in 2008. A standardized review was performed by two reviewers. Twenty eight ergonomic intervention papers met the inclusion criteria and were fully reviewed. Data and trends from the reviewed papers were summarized specifically looking at the research designs used, the outcome measures used, if statistical power was mentioned, if a rationale for sample size was reported, if standardized and un-standardized effect sizes were reported, if confidence intervals were reported, the alpha levels used, if pair-wise correlation values were provided, if mean values and standard deviations were provided for all measures and the location of the studies. Also, the studies were rated based on the outcomes of their intervention into one of three categories (shown to be effective, inconclusive and not shown to be effective). Between these three groupings comparisons of post hoc power, standardized effect sizes, un-standardized effect sizes and coefficients of variation were made. The results indicate that in general, a lack of statistical power is indeed a concern and may be due to the sample sizes used, effect sizes produced, extremely high variability in some of the measures, the lack of attention paid to statistical power during research design and the lack of appropriate statistical reporting guidelines in journals where ergonomic intervention research may be published. A total of 69.6% of studies reviewed had a majority of measures with less than .50 power and 71.4% of all measures used had CVs of > .20.

As awareness of the costs of workplace injury and illness continues to grow, there has been an increased demand for effective ergonomic interventions to reduce the prevalence of musculoskeletal disorders (MSDs). The goal of ergonomic interventions is to reduce exposures (mechanical and psychosocial); however there is conflicting evidence about the impact of these interventions as many studies produce inconclusive or conflicting results. In order to provide a clearer picture of the effectiveness of these interventions, we must find out if methodological issues, particularly statistical power, are limiting this research. The purpose of this study was to review and examine factors influencing statistical power in ergonomic intervention papers from five peer reviewed journals in 2008. A standardized review was performed by two reviewers. Twenty eight ergonomic intervention papers met the inclusion criteria and were fully reviewed. Data and trends from the reviewed papers were summarized specifically looking at the research designs used, the outcome measures used, if statistical power was mentioned, if a rationale for sample size was reported, if standardized and un-standardized effect sizes were reported, if confidence intervals were reported, the alpha levels used, if pair-wise correlation values were provided, if mean values and standard deviations were provided for all measures and the location of the studies. Also, the studies were rated based on the outcomes of their intervention into one of three categories (shown to be effective, inconclusive and not shown to be effective). Between these three groupings comparisons of post hoc power, standardized effect sizes, un-standardized effect sizes and coefficients of variation were made. The results indicate that in general, a lack of statistical power is indeed a concern and may be due to the sample sizes used, effect sizes produced, extremely high variability in some of the measures, the lack of attention paid to statistical power during research design and the lack of appropriate statistical reporting guidelines in journals where ergonomic intervention research may be published. A total of 69.6% of studies reviewed had a majority of measures with less than .50 power and 71.4% of all measures used had CVs of > .20.

abstract: Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed. / Dissertation/Thesis / M.A. Psychology 2013

No description available.

Power and effect size analysis are important methods in the psychological sciences. It is well known that classical statistical tests are not robust with respect to power and type II error. However, relatively little attention has been paid in the psychological literature to the effect that non-normality and outliers have on the power of a given statistical test (Wilcox, 1998). Robust measures of location exist that provide much more powerful tests of statistical hypotheses, but their usefulness in power estimation for sample size selection, with real data, is largely unknown. Furthermore, practical approaches to power planning (Cohen, 1988) usually focus on normal theory settings and in general do not make available nonparametric approaches to power and effect size estimation. Beran (1986) proved that it is possible to nonparametrically estimate power for a given statistical test using bootstrap methods (Efron, 1993). However, this method is not widely known or utilized in data analysis settings. This research study examined the practical importance of combining robust measures of location with nonparametric power analysis. Simulation and analysis of real world data sets are used. The present study found that: 1) bootstrap confidence intervals using Mestimators gave shorter confidence intervals than the normal theory counterpart whenever the data had heavy tailed distributions; 2) bootstrap empirical power is higher for Mestimators than the normal theory counterpart when the data had heavy tailed distributions; 3) the smoothed bootstrap controls type I error rate (less than 6%) under the null hypothesis for small sample sizes; and 4) Robust effect sizes can be used in conjuction with Cohen's (1988) power tables to get more realistic sample sizes given that the data distribution has heavy tails.

The method of combination p-values from multiple tests is the foundation for some studies like meta-analysis and detection of signal. There are tremendous methods have been developed and applied like minimum p-values, Cauchy Combination, goodness-of-fit combination and Fisher’s combination. In this paper, I tested their ability to detect signals which is related to real case in biology to find out significant single-nucleotide polymorphisms (SNPs). I simulated p-values for SNPs logistics regression model and test 7 combination methods’ power performance in different setting conditions. I compared sparse or dense signals, dependent or independent and combine them in gene-level or pathway-level. One method based on Fisher’s combination called Omni-TFisher is ideal for most of the situations. Recent years, genome-wide association studies (GWASs) focused on BMD-related SNPs at gene significance level. In this paper I used Omni-TFisher to analyses real data on haplotype blocks. As a result, haplotype blocks can find more SNPs in non-coding and intergeneric regions than gene-based and save computational complexity. It finds out not only known genes, but also other genes need further verification.

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis. This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits. The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties. In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

Thesis (Ph.D.)--Ohio University, June, 2010. / Title from PDF t.p. Includes bibliographical references.

Thesis (Ph.D.)--Ohio University, August, 2006. / Title from PDF t.p. Includes bibliographical references.