Master of Science / Department of Statistics / Dallas W. Johnson / Meta analysis is a statistical technique for synthesizing of results obtained from multiple studies. It is the process of combining, summarizing, and reanalyzing previous quantitative research. It yields a quantitative summary of the pooled results. Decisions of the validity of a hypothesis cannot be based on the results of a single study, because results typically vary from one study to the next. Traditional methods do not allow involving more than a few studies. Meta analysis provides certain procedures to synthesize data across studies. When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect. When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation. The amount of accumulated information in fast developing fields of science such as biology, medicine, education, pharmacology, physics, etc. increased very quickly after the Second World War. This lead to large amounts of literature which was not systematized. One problem in education might include ten independent studies. All of the studies might be performed by different researchers, using different techniques, and different measurements. The idea of integrating the research literature was proposed by Glass (1976, 1977). He referred it as the meta analysis of research. There are three major meta analysis approaches: combining significance levels, combining estimates of effect size for fixed effect size models and random effect size models, and vote-counting method.

Class of 2005 Abstract / Objective: The purpose of this study was to explore whether current meta-analyses on exercise interventions assess adherence and/or compliance of the studies included in the meta-analyses and to determine if subject adherence had any effect on outcomes of the analyses. Methods: Data was collected through a search of the MEDLINE database using the key words exercise, adherence, compliance, clinical trials, and meta-analysis. Data on study title, author, number of studies screened, number in meta-analysis, range of sample sizes, total number of subjects, primary intervention, primary outcome, how study quality was assessed, how adherence was assessed, whether adherence was used as a control variable, and did adherence affect the outcome was recorded on a paper and pencil data extraction form. Data was analyzed by constructing a table describing the meta-analyses and calculating the number and percent of analyses that included adherence. The table allowed for the evaluation of the strength and methodology of each piece of literature with respect to acknowledging adherence as a significant variable in the strength and legitimacy of each analysis. Results: Nineteen meta-analyses met our search criteria and were evaluated. Five of the nineteen meta-analyses (26 percent) described a method for assessing adherence. It was found that none of these used adherence as a control variable. Four of the nineteen meta-analyses did not assess the quality of the studies contained within the analysis. One of these meta-analyses suggested that adherence may have confounded outcomes, but did not provide any data to address their concerns. Conclusions: In meta-analyses, adherence is unlikely to be addressed. Current meta-analyses frequently lack methods for assessing adherence, and do not use adherence as a control variable. Whether adherence to exercise regimens affects outcomes cannot be determined from current meta-analyses.

Background: Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19. Objective: To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19. Methods: We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test. Results: We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P <.001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P <.001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2 = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P <.001; mortality: OR 10.42 95% CI 7.73 to 14.06; P =.005). Conclusions: Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients. / Revisión por pares

Background: In pain relief research, meta-analyses often combine continuous outcomes from various studies using mean differences. However, this approach can be difficult to interpret clinically. An alternative method involves aggregating the risk difference for patients who achieve a minimally important difference (MID) in pain reduction. The challenge is that many trials do not report responder analyses, necessitating continuous data conversion. Objective: To conduct a simulation study assessing the performance of four proposed methods for estimating the pooled risk difference (RD) of achieving the MID in meta-analyses of pain measured on a 10cm visual analogue scale (VAS). Methods: Individual patient data for VAS pain scores were simulated across 4,752 scenarios varying the treatment effect as change score in the intervention (-1.0 to 4.0) and control (-1.0 to 3.0) groups, study sample size (10-1000), number of studies per meta-analysis (3 to 30), shape of distribution (normal or skewed), and MID (1.0 or 1.5). The true pooled RD and 95% confidence interval (CI) were calculated from the simulated individual data. Four methods were evaluated: calculating RD based on pooled 1) median mean differences, 2) unweighted average differences, 3) weighted average differences, and 4) calculating RD for each individual study and then meta-analysing RDs. Bias, mean squared error, confidence interval (CI) coverage of true value, and empirical standard error (SE), and model-based SE were evaluated. Results: The median method showed the lowest bias (2.048; 95% CI: 1.759-2.338), while the individual method demonstrated the lowest RMSE (4.852; 95% CI: 4.661-5.044), empirical SE (0.148; 95% CI: 0.141-0.154), and model-based SE (2.198; 95% CI: 2.108-2.288), and highest CI coverage (55.717%; 95% CI: 53.185-58.250%). Differences between methods were minimal and not statistically significant. Performance was optimal when treatment effects were similar between groups and declined with increasing effect size differences. All methods performed poorly with skewed distributions. Conclusion: While the evaluated methods can provide useful estimates in many scenarios, they should be used cautiously, especially for large treatment effects or non-normal data. Researchers should prioritize conducting and reporting responder analyses in primary studies to reduce reliance on these estimation methods in meta-analyses. / Thesis / Master of Science (MSc) / Background: In pain relief research, meta-analyses often combine continuous outcomes from various studies using mean differences. However, this approach can be difficult to interpret clinically. An alternative method involves aggregating the risk difference for patients who achieve a minimally important difference (MID) in pain reduction. The challenge is that many trials do not report responder analyses, necessitating continuous data conversion. Objective: To conduct a simulation study assessing the performance of four proposed methods for estimating the pooled risk difference (RD) of achieving the MID in meta-analyses of pain measured on a 10cm visual analogue scale (VAS). Methods: Individual patient data for VAS pain scores were simulated across 4,752 scenarios varying the treatment effect as change score in the intervention (-1.0 to 4.0) and control (-1.0 to 3.0) groups, study sample size (10-1000), number of studies per meta-analysis (3 to 30), shape of distribution (normal or skewed), and MID (1.0 or 1.5). The true pooled RD and 95% confidence interval (CI) were calculated from the simulated individual data. Four methods were evaluated: calculating RD based on pooled 1) median mean differences, 2) unweighted average differences, 3) weighted average differences, and 4) calculating RD for each individual study and then meta-analysing RDs. Bias, mean squared error, confidence interval (CI) coverage of true value, and empirical standard error (SE), and model-based SE were evaluated. Results: The median method showed the lowest bias (2.048; 95% CI: 1.759-2.338), while the individual method demonstrated the lowest RMSE (4.852; 95% CI: 4.661-5.044), empirical SE (0.148; 95% CI: 0.141-0.154), and model-based SE (2.198; 95% CI: 2.108-2.288), and highest CI coverage (55.717%; 95% CI: 53.185-58.250%). Differences between methods were minimal and not statistically significant. Performance was optimal when treatment effects were similar between groups and declined with increasing effect size differences. All methods performed poorly with skewed distributions. Conclusion: While the evaluated methods can provide useful estimates in many scenarios, they should be used cautiously, especially for large treatment effects or non-normal data. Researchers should prioritize conducting and reporting responder analyses in primary studies to reduce reliance on these estimation methods in meta-analyses.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / A few years ago XidML was introduced as an open XML standard for capturing the meta-data associated with flight test instrumentation (FTI). This meta-data schema was broken down into elements for Parameter (name, range, units, offset-binary), Instrument (name, serial number, misses-to loss), Package (bits per word, words per minor-frame, rate) and Link (name, type) and so on. XidML remains one of the only published schema for FTI meta-data and with XidML 3.0 many simplifications have been introduced along with support for nested tree structures and a single instrument schema allowing anyone to define the validation for instruments from any vendor. This paper introduces the XidML schema and describers the benefits of XidML 3.0 in particular. It begins by giving a brief description of what XidML is and describes its history and motivation. The paper then outlines the main differences between XidML-3.0 and earlier versions, and how the XidML schema has been further refined to meet the challenges faced by the FTI community. As an example of usage the FTIManager software developed at Eurocopter will be briefly presented in order to illustrate the XidML ability to describe a multi-vendor FTI configuration.

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This study examined the statistical consequences of employing various methods of computing and cumulating effect sizes in meta-analysis. Six methods of computing effect size, and three techniques for combining study outcomes, were compared. Effect size metrics were calculated with one-group and pooled standardizing denominators, corrected for bias and for unreliability of measurement, and weighted by sample size and by sample variance. Cumulating techniques employed as units of analysis the effect size, the study, and an average study effect. In order to determine whether outcomes might vary with the size of the meta-analysis, mean effect sizes were also compared for two smaller subsets of studies. An existing meta-analysis of 60 studies examining the effectiveness of computer-based instruction was used as a data base for this investigation. Recomputation of the original study data under the six different effect size formulas showed no significant difference among the metrics. Maintaining the independence of the data by using only one effect size per study, whether a single or averaged effect, produced a higher mean effect size than averaging all effect sizes together, although the difference did not reach statistical significance. The sampling distribution of effect size means approached that of the population of 60 studies for subsets consisting of 40 studies, but not for subsets of 20 studies. Results of this study indicated that the researcher may choose any of the methods for effect size calculation or cumulation without fear of biasing the outcome of the metaanalysis. If weighted effect sizes are to be used, care must be taken to avoid giving undue influence to studies which may have large sample sizes, but not necessarily be the most meaningful, theoretically representative, or elegantly designed. It is important for the researcher to locate all relevant studies on the topic under investigation, since selective or even random sampling may bias the results of small meta-analyses.

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.

This thesis provides the first meta-analysis investigating the effect of corporate tax competition among states, with special focus on the effect of the corporate tax rate change in competing country on the corporate tax rate in the home country. It examines 523 estimates from 20 published studies and working papers. Results of the meta-analysis show an evidence of substantial publication selectivity: researchers tend to discard negative and insignificant estimates, which overvalues the estimated effect size. Conducted precision effect test failed to find the evidence for the existence of a genuine effect of corporate tax competition. Empirical analysis shows that differences in the measurement of statutory and effective tax rate matter, thus the analysis was conducted on two separate sub-samples. Meta-regression analysis have found significant impact of variables related to publication bias for both sub-samples. Next to it, the results provide an evidence of significant influence of politically orientated controls, especially of the variable controlling whether or not there were elections in the particular year and state in case when the corporate tax rate changed. Powered by TCPDF (www.tcpdf.org)

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