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One goal of the present meta-analysis was to assess if attachment-based preventative interventions are effective at fostering attachment security and preventing problems associated with insecure and disorganized attachment. Another goal was to determine what factors are associated with larger effects. Studies were considered eligible if they were a randomized controlled trial, had an attachment-based preventative intervention for children, and had a measure of attachment security, behaviour problems, language development, or emotional regulation. A random effects model was used and a total of 22 studies were included in the meta-analysis. The results of the meta-analysis indicated that attachment-based prevention interventions produced a reliable small to moderate change (d = .37) in children’s attachment security and problems associated with insecure and disorganized attachment. Potential moderating variables were also examined. Total number of sessions and the proportion of single caregivers was associated with a larger effect.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Marcia Rosal, Florida State University, School of Visual Arts and Dance, Dept. of Art Education. Title and description from dissertation home page (viewed June 8, 2005). Document formatted into pages; contains xii, 142 pages. Includes bibliographical references.

Thesis (D.A.)--Illinois State University, 1985. / Title from title page screen, viewed June 30, 2005. Dissertation Committee: Bernard McCarney (chair), Mathew Morey, Ronald Halinski, Alan Dillingham, Anthony Ostrosky. Includes bibliographical references (leaves 162-165) and abstract. Also available in print.

Thesis (M.M.) Florida State University, 2005. / Advisor: Jayne M. Standley, Florida State University, College of Music. Title and description from dissertation home page (viewed 5-16-2007). Document formatted into pages; contains 31 pages. Includes biographical sketch. Includes bibliographical references.

目的：評價中西醫結合治療肝纖維化的療效。 方法：以＂肝纖維化＂，或＂Hepatic Fibrosis＂，並且＂中醫＂，或＂中西醫＂，或＂中藥＂或＂Chinese medicine＂為檢索詞，在中國期刊全文資料庫（CNKI）、中文科技期刊全文資料庫維普資訊（VIP）、萬方資料知識平臺、PubMed、EmBase檢索近20年（1995-2015年）發表的有關中西醫結合治療肝纖維化的臨床研究文獻。設定文獻選入標準及文獻剔除標準。選取隨機對照試驗（RCT）。對文獻進行Jadad評分。並提取文獻資料資料。評分大於或等於2 者納人meta分析。採用Revman5.3軟體進行Meta分析。採用隨機效應模型，應用倒漏斗圖檢測是否存在發表偏倚。 結果：檢索出文獻共537篇，根據文獻選入及剔除標準，共12篇納入分析。總病例數1350.對照組620例，試驗組730例。以肝纖維化血清學指標血清透明質酸酶（HA）、血清層粘蛋白（LN）、III型前膠原（PCIIl）、IV型膠原(IV-C）為分析指標。中西醫結合治療肝纖維化HA 的合併效應量WMD=-61.87，95%可信區間為[-79.74,-44.09]。Z=6.79(p<0.00001）。差異有統計學意義：治療組在降低HA方面較對照組有明顯優勢。中西醫結合治療肝纖維化LN的合併效應量WMD=-43. 21，95%可信區間為[-57.61，-28.81]。Z=5.88(p

Class of 2014 Abstract / Specific Aims: To identify the factors that affects the potency of pharmacists’ interventions. Methods: Literature search was based on keywords and Mesh terms in eight different databases. The inclusion criteria were evidence of pharmacist involvement in direct patient care, patient-related therapeutic outcomes, studies done in the United States, randomized controlled trials, studies with reported number of subjects in the intervention and control group and reported means and standard deviations of therapeutic outcomes. For the study selection and data extraction, two students independently reviewed each study and met to resolve any discrepancies. In addition, each study was assigned a potency score using the potency tool. Data extraction included: pharmacists’ interventions (technical, behavioral, educational, and affective), patient characteristics, and therapeutic outcomes. The standardized mean difference (SMD) was calculated; studies with SMD ≥ -0.3 formed the low impact group (controls) and studies with SMD  -0.8 formed the high impact group (cases). Main Results: The included randomized control trials (N=11) were conducted in a variety of settings from ambulatory clinics to hospital. The high impact group was favored in the educational category (ES=0.88, p=0.18) while the low impact group was favored in the behavioral category (ES=-0.19, p=0.81). In general, there was a difference between the high impact and low impact (ES=0.82, p=0.37) groups with the high impact group being favored. Conclusion: There is a difference between the low impact and high impact groups, but it is unclear which pharmacist interventions have a significant impact on therapeutic outcomes.

Class of 2013 Abstract / Specific Aims: To assess the effectiveness of modafinil as an adjunct agent in the treatment of major depression and depression-related fatigue. Methods Seven databases were searched for articles that met predetermined inclusion criteria and reported sufficient data. Meta-analysis was employed to synthesize study findings, with standardized mean difference (SMD) being the primary summary measure. The I-squared statistic was used to evaluate heterogeneity among studies. Additionally, publication bias was assessed via funnel plots and Kendall’s tau.      Main Results: Ten studies (N = 848) were included in the Hamilton Depression Rating Scale (HAM-D) meta-analysis, composed of 5 RCTs and 5 pre-post studies. The pooled SMD was -0.67, a moderate effect indicating an improvement in depression scores. However, the overall SMD varied when stratified by study design; pre-post studies showed a large pooled effect (SMD = -1.54) that reached significance, whereas RCT's displayed a moderate effect (SMD = -0.41) that was not significant. Additonally, heterogeneity was substantial (I-squared = 91.54) among all studies, and publication bias was suggested by the funnel plot and Kendall's tau. Regarding modafinil and fatigue, the Epworth Sleepiness Scale (ESS) meta-analysis had a small but statistically signficant overall SMD (-0.23; p = 0.03), and the Fatigue Severity Scale (FSS) meta-analysis yielded an overall SMD which was not significant (p = 0.24). Similar to the HAM-D analysis, the overall SMD varied between study designs. Conclusion: The effect of modafinil on major depressive disorder is unclear, as the findings are largely variable and the impact of modafinil was stratified by study design.

Class of 2013 Abstract / Specific Aims: To evaluate efficacy of the current pharmacological treatment of depression in the adult advanced and terminal cancer patient population. Methods: Trials assessing a pharmacological treatment for depression in cancer patients were found using MEDLINE and PSYCINFO databases. Comprehensive Meta-Analysis software was used to generate a random effects model forest plot, a funnel plot, classical fail-safe N, I2, and Kendall’s tau. Main Results: Ten studies, with an aggregate population was 1,167 patients, were used in this meta-analysis to generate a random effects variance model. The effect size was 0.42 +/- 0.09 (p < 0.01). I2 for aggregate data was 66.16 (p < 0.01). Kendall’s tau with continuity correction was 0.272 (P-value [2-tailed] = .244). The classic fail-safe N was 151 (p < 0.1). Three studies reported a significant increase in adverse effects between treatment and comparison group. Conclusion: Antidepressants were shown to have a moderate effect size when treating depression in advanced and terminal cancer patients. These medications were well tolerated. Antidepressant medications are beneficial as part of a comprehensive treatment plan for cancer patients diagnosed with depression.

Meta-analysis is an important statistical tool for systematic reviews and evidence-based medicine. Extracting the observed effect sizes, assessing the magnitude of heterogeneity, choosing the suitable statistical model, and interpreting the summary effect size are four key steps in meta-analysis. It is known that each of the above steps has its own unique characteristics and may require some specific attention. As an example, the observed effect sizes from individual studies may not be reported in the same scale and hence cannot be combined directly. Another example is on selecting a model for meta-analysis from the common-effect model and the random-effects model. When a meta-analysis contains only few studies, the common-effect model and the random-effects model will often lead to misleading or unreliable results. In the first part of the thesis, we give a brief introduction on evidence-based medicine, systematic reviews and meta-analysis. We will also show their practical importance, display their relationships, and present a motivating example for conducting a meta-analysis. In Chapter 2, we first review the common effect sizes in meta-analysis for both continuous data and binary data. How to combine different categories of effect sizes is a critical issue after extracting the observed effect sizes from the clinical studies in the literature. For continuous data, researchers have recently proposed methods that transform the five number summary to the sample mean and standard deviation (Hozo et al., 2005; Wan et al., 2014; Luo et al., 2018). For binary data, the transformation from the odds ratio (OR) to the relative risk (RR) in the cohort study was proposed by Zhang and Yu (1998). To the best of our knowledge, however, there is little work in the literature that converts OR to RR in the case-control study. In view of this, we establish a new formula for this transformation to fulfill the gap. The performance of the new method will be examined through simulations and real data analysis. Our method and formulas can not only handle meta-analyses with different effect sizes, but also offer some insights for medical researchers to further understand the meaning of OR and RR in both cohort and case-control studies. In Chapter 3, we first give a brief introduction on the three available models in meta-analysis: the common-effect model, the random-effects model, and the fixed-effects model. When a meta-analysis contains only few studies, the common-effect model and the random-effects model will often lead to misleading or unreliable results. In contrast, the fixed-effects model is capable to provide a good compromise between the existing two models. In this chapter, we propose to further improve the estimation accuracy of the average effect in the fixed-effects model by assigning different weight for each study as well as fully utilizing the information in the within-study variances. Through theory and simulation, we demonstrate that the fixed-effects model can serve as the most convincing model for meta-analysis with few studies. And most importantly, with a total of three models, we expect that meta-analysis can be conducted more flexibly, more meaningfully, and more accurately. In Chapter 4, we first give a brief introduction on the heterogeneity in meta-analysis. We then review the methods for quantifying heterogeneity in three directions as follows: the tests for heterogeneity, the estimates of the between-study variance, and the measures of the impact of heterogeneity. Note that most existing methods were derived under the assumption of known within-study variances. In practice, however, a direct use of the reported within-study variance estimates may largely reduce the power of the tests and also lower the accuracy of the estimates, especially when the sample sizes in some studies are not sufficiently large. To overcome this problem, we propose a family of shrinkage estimators for the within-study variances that are able to borrow information across the studies, and derive the optimal shrinkage parameters under the Stein loss function. We then apply the new estimates of the within-study variances to some well-known methods for measuring heterogeneity. Simulation studies and real data examples show that our shrinkage estimators can dramatically reduce the estimation bias and hence improve the exiting literature. Keywords: Common-effect model, Effect size, Fixed-effects model, Heterogeneity, Meta-analysis, Odds ratio, Random-effects model, Relative risk, Risk ratio