Interactions between cyanobacteria and herbivorous grazers play an important role in mediating the responses of freshwater phytoplankton assemblages to nutrient enrichment and top-down manipulation. Negative consequences associated with these interactions include dangerous blooms of harmful blue-green algae that have been implicated in the sickness and death of fishes, livestock, and, in extreme cases, humans. Frequently cited mechanisms influencing the interactions between grazers and cyanobacteria include cyanobacterial toxicity and morphology. To tease apart the importance of these mechanisms, I used meta-analysis to quantitatively synthesize the available literature on this topic. In addition, I conducted several experiments using novel techniques to determine the effect that cyanobacterial secondary metabolites from the bloom-forming cyanobacterium,

Vitamin K has a negligible effect on bone mineral density (BMD) and a large but uncertain effect on fractures. The three studies in the thesis explored uncertainty about the effect of vitamin K on fractures using the methods of economic evaluation and Bayesian meta-analysis. In study 1, a Markov probabilistic microsimulation model was developed for a hypothetical cohort of 50-year-old postmenopausal women without osteoporosis. This was a fracture incidence-based model, populated with data from the literature. It was used to examine the cost-effectiveness of two supplementation strategies over a lifetime horizon. We compared vitamin K2 (or vitamin K1) concurrent with vitamin D3 and calcium versus vitamin D3 and calcium alone. Study 2 included a systematic review, and classical and Bayesian univariate meta-analyses to determine the efficacies of the K vitamins on BMD or fractures in current and future trials. Study 3 used Bayesian bivariate random-effects meta-analysis to jointly model the treatment effects on two correlated bone outcomes. We compared the estimates from the univariate and bivariate meta-analyses and explored how these results would change the conclusions of the cost-effectiveness analysis. The strategies including vitamin K were highly cost-effective at willingness-to-pay of $50,000/QALY (quality-adjusted life year); however, the results were most sensitive to changes in the efficacy of vitamin K. The univariate meta-analyses showed large uncertainties in the anti-fracture effects of vitamin K2 in current and future trials. The bivariate 95% credible intervals were considerably narrower than those from the univariate meta-analyses. Using future odds ratios from the bivariate meta-analyses, vitamin K2 cost more than $100,000/QALY while vitamin K1 was cost-saving. Our analyses found substantial uncertainty around the estimates of the vitamin K effect on fractures. We recommend against routine use of vitamin K for fracture prevention. Bayesian bivariate meta-analysis accounts for all available information and should be considered when the treatment effects are measured on two correlated outcomes.

Vitamin K has a negligible effect on bone mineral density (BMD) and a large but uncertain effect on fractures. The three studies in the thesis explored uncertainty about the effect of vitamin K on fractures using the methods of economic evaluation and Bayesian meta-analysis. In study 1, a Markov probabilistic microsimulation model was developed for a hypothetical cohort of 50-year-old postmenopausal women without osteoporosis. This was a fracture incidence-based model, populated with data from the literature. It was used to examine the cost-effectiveness of two supplementation strategies over a lifetime horizon. We compared vitamin K2 (or vitamin K1) concurrent with vitamin D3 and calcium versus vitamin D3 and calcium alone. Study 2 included a systematic review, and classical and Bayesian univariate meta-analyses to determine the efficacies of the K vitamins on BMD or fractures in current and future trials. Study 3 used Bayesian bivariate random-effects meta-analysis to jointly model the treatment effects on two correlated bone outcomes. We compared the estimates from the univariate and bivariate meta-analyses and explored how these results would change the conclusions of the cost-effectiveness analysis. The strategies including vitamin K were highly cost-effective at willingness-to-pay of $50,000/QALY (quality-adjusted life year); however, the results were most sensitive to changes in the efficacy of vitamin K. The univariate meta-analyses showed large uncertainties in the anti-fracture effects of vitamin K2 in current and future trials. The bivariate 95% credible intervals were considerably narrower than those from the univariate meta-analyses. Using future odds ratios from the bivariate meta-analyses, vitamin K2 cost more than $100,000/QALY while vitamin K1 was cost-saving. Our analyses found substantial uncertainty around the estimates of the vitamin K effect on fractures. We recommend against routine use of vitamin K for fracture prevention. Bayesian bivariate meta-analysis accounts for all available information and should be considered when the treatment effects are measured on two correlated outcomes.

Thesis (D.P.H.)--University of Michigan.

Dissertation (D.P.H.)--University of Michigan.

Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.

Reliability generalization (RG) is a method for meta-analysis of reliability coefficients to estimate average score reliability across studies, determine variation in reliability, and identify study-level moderator variables influencing score reliability. A total of 107 peer-reviewed RG studies published from 1998 to 2013 were systematically reviewed to characterize the meta-analytic methods employed and to evaluate quality of reporting practice against standards for transparency in meta-analysis reporting. Most commonly, RG studies meta-analyzed alpha coefficients, which were synthesized using an unweighted, fixed-effects model applied to untransformed coefficients. Moderator analyses most frequently included multiple regression and bivariate correlations employing a fixed-effects model on untransformed, unweighted coefficients. Based on a unit-weighted scoring system, mean reporting quality for RG studies was statistically less than that for a comparison study of 198 meta-analyses in the organizational sciences across 42 indicators; however, means were not statistically significantly different between the two studies when evaluating reporting quality on 18 indicators deemed essential to ethical reporting practice in meta-analyses. Since its inception a wide variety of statistical methods have been applied to RG, and meta-analysis of reliability coefficients has extended to fields outside of psychological measurement, such as medicine and business. A set of guidelines for conducting and reporting RG studies is provided.

The burden of depression requires modalities other than individual psychotherapy if we are to reduce it. Over the past two decades preventative programs for depression have been developed and refined for different populations. The six years since the last meta-analysis of preventative interventions—inclusive of all program types—have seen a number of new studies. The current study used the greater statistical power provided by these new studies to analyze moderators of, and sub-group differences in, the effect of these interventions on depression. Moreover, this meta-analysis synthesized effect sizes for outcomes other than, but often related to, depression (e.g., anxiety) and for within-group change scores with the goal of better informing program implementation and evaluation. Twenty-nine studies met inclusion criteria and indicated that small, robust effects exist for reductions in depression diagnoses and symptomatology. Significant effects were also observed for anxiety, general health, and social functioning.

Interferon gamma (IFN-γ) regulation of macrophages plays an essential role in innate immunity and pathogenicity of viral infections by directing large and small genome-wide changes in the transcriptional program of macrophages. Smaller changes at the transcriptional level are difficult to detect but can have profound biological effects, motivating the hypothesis of this thesis that responses of macrophages to immune activation by IFN-γ include small quantitative changes that are masked by noise but represent meaningful transcriptional systems in pathways against infection. To test this hypothesis, statistical meta-analysis of microarray studies is investigated as a tool to obtain the necessary increase in analysis sensitivity. Three meta-analysis models (Effect size model, Rank Product model, Fisher’s sum of logs) and three further modified versions were applied to a heterogeneous set of four microarray studies on the effect of IFN-γ on murine macrophages. Performance assessments include recovery of known biology and are followed by development of novel biological hypotheses through secondary analysis of meta-analysis outcomes in context of independent biological data sources. A separate network analysis of a microarray time course study investigate s if gene sets with coordinated time-dependent relationships overlap can also identify subtle IFN-γ related transcriptional changes in macrophages that match those identified through meta-analysis. It was found that all meta-analysis models can identify biologically meaningful transcription at enhanced sensitivity levels, with slightly improved performance advantages for a non-parametric model (Rank Product meta-analysis). Meta-analysis yielded consistently regulated genes, hidden in individual microarray studies, related to sterol biosynthesis (Stard3, Pgrmc1, Galnt6, Rab11a, Golga4, Lrp10), implicated in cross-talk between type II and type I interferon or IL-10 signalling (Tbk1, Ikbke, Clic4, Ptpre, Batf), and circadian rhythm (Csnk1e). Further network analysis confirms that meta-analysis findings are highly concentrated in a distinct immune response cluster of co-expressed genes, and also identifies global expression modularisation in IFN-γ treated macrophages, pointing to Trafd1 as a central anti-correlated node topologically linked to interactions with down-regulated sterol biosynthesis pathway members. Outcomes from this thesis suggest that small transcriptional changes in IFN-γ activated macrophages can be detected by enhancing sensitivity through combination of multiple microarray studies. Together with use of bioinformatical resources, independent data sets and network analysis, further validation assigns a potential role for low or variable transcription genes in linking type II interferon signalling to type I and TLR signalling, as well as the sterol metabolic network.

The effects of air pollutants CO, NO₂, O₃, PM₁₀ and SO₂ on congenital heart anomalies are represented by the odds ratio of each disease per unit increase in the concentration of each pollutant. In this study, the effects of air pollutants are summarized using multivariate GLS approach with correlation between outcomes being taken into account, where the correlations are sampled from uniform [-1,1]. Meta-analysis conducted here found no statistically significant increase in odds ratio of any disease. This result is different from what Vrijheid et al. 2011 suggested when correlation is not considered using the same set of data. The difference in conclusions from the two meta-analysis indicate that correlation between outcomes may play an important role when synthesizing effect sizes. Thus, before conduct meta-analysis, a thorough consideration about whether to incorporate the correlation in synthesizing should be given. / text