scholarly journals The Ontology of Biological and Clinical Statistics (OBCS)-based statistical method standardization and meta-analysis of host responses to yellow fever vaccines

2017 ◽  
Vol 5 (4) ◽  
pp. 291-301 ◽  
Author(s):  
Jie Zheng ◽  
Huan Li ◽  
Qingzhi Liu ◽  
Yongqun He
Keyword(s):  
Statistical Method ◽  
Yellow Fever ◽  
Meta Analysis ◽  
Host Responses ◽  
Clinical Statistics

scholarly journals A Statistical Method for Synthesizing Meta-Analyses

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Liansheng Larry Tang ◽  
Michael Caudy ◽  
Faye Taxman
Keyword(s):  
Statistical Method ◽  
Effect Size ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Summary Effect ◽  
Different Types ◽  
Meta Analyses ◽  
Size Estimates ◽  
Similar Search

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

Can Meta-Analysis Make Feminist Transformations In Psychology?

1994 ◽  
Vol 18 (4) ◽  
pp. 451-462 ◽  
Author(s):  
Janet Shibley Hyde
Keyword(s):  
Gender Differences ◽  
Gender Roles ◽  
Statistical Method ◽  
Spatial Ability ◽  
Meta Analysis ◽  
Verbal Ability ◽  
Helping Behavior ◽  
Mathematics Performance ◽  
Popular Media ◽  
Race Ethnicity

Meta-analysis is a statistical method for literature reviewing. Metaanalyses of gender differences in verbal ability, spatial ability, mathematics performance, helping behavior, and sexuality illustrate the ways in which this technique can illuminate research on gender differences. Meta-analysis can make feminist transformations in psychology by: (a) challenging long-standing beliefs in gender differences, (b) demonstrating the extent to which gendered behavior is context-dependent and the product of gender roles, (c) examining the intersection between gender and race/ethnicity, and (d) providing powerful data to counter assertions of difference and female inferiority that proliferate in the popular media.

scholarly journals The Incidence and Mortality of Yellow Fever in Africa – A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Akuoma U Nwaiwu ◽  
Alfred Musekiwa ◽  
Jacques L. Tamuzi ◽  
Evanson Z Sambala ◽  
Peter S Nyasulu
Keyword(s):  
Yellow Fever ◽  
Early Recognition ◽  
Meta Analysis ◽  
Case Fatality ◽  
Cochrane Library ◽  
African Countries ◽  
Targeted Interventions ◽  
Diagnostic Measures ◽  
Incidence And Mortality ◽  
And Control

Abstract BackgroundUnderstanding the occurrence of yellow fever epidemics is critical for targeted interventions and control efforts to reduce the burden of disease. We assessed data on the yellow fever incidence and mortality rates in Africa.MethodsWe searched the Cochrane Library, SCOPUS, MEDLINE, CINAHL, PubMed, Embase, Africa-wide and Web of science databases from 1 January 1975 to 30th October 2020. Two authors extracted data from included studies independently and conducted a meta-analysis.ResultsOf 840 studies identified, 12 studies were deemed eligible for inclusion. The incidence of yellow fever per 100,000 population ranged from <1 case in Nigeria, < 3 cases in Uganda, 13 cases in DRC, 27 cases in Kenya, 40 cases in Ethiopia, 46 cases in Gambia, 1,267 cases in Senegal, and 10,350 cases in Ghana. Case fatality rate associated with yellow fever outbreaks ranged from 10% in Ghana to 86% in Nigeria. The mortality rate ranged from 0.1/100,000 in Nigeria to 2,200/100,000 in Ghana. ConclusionThe yellow fever incidence rate is quite constant; in contrast, the fatality rates vary widely across African countries over the study period. Standardized demographic health surveys and surveillance as well as accurate diagnostic measures are essential for early recognition, treatment and control.

Meta-analysis

ESC CardioMed ◽  
2018 ◽  
pp. 3092-3098
Author(s):  
Natalie Staplin ◽  
Colin Baigent
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Statistical Method ◽  
Cardiovascular Events ◽  
Randomized Trials ◽  
Meta Analysis ◽  
Treatment Decisions ◽  
Meta Analyses ◽  
Effective Treatments

The term ‘meta-analysis’ refers to a statistical method for combining the results of several (often many) studies or experiments in order to arrive at an overall conclusion about the size and variability of the measure of interest. In the context of cardiovascular disease, such studies are most often randomized trials of therapies for the prevention or treatment of cardiovascular events, such as myocardial infarction or stroke. The specialty has now witnessed several decades of success in identifying effective treatments for cardiovascular diseases, and the technique of meta-analysis of randomized trials has played an important role in this success. Not all meta-analyses are made equal, however, and it is important to be aware of the limitations of the method. This chapter considers how the technique can be best employed to guide treatment decisions, while also highlighting the limitations of meta-analysis when the information available is inadequate or potentially biased.

scholarly journals A Statistical Method to Base Nutrient Recommendations on Meta-Analysis of Intake and Health-Related Status Biomarkers

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e93171 ◽  
Author(s):  
Hilko van der Voet ◽  
Waldo J. de Boer ◽  
Olga W. Souverein ◽  
Esmée L. Doets ◽  
Pieter van 't Veer
Keyword(s):  
Statistical Method ◽  
Meta Analysis ◽  
Health Related

scholarly journals Estimating case fatality risk of severe Yellow Fever cases: systematic literature review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joseph L. Servadio ◽  
Claudia Muñoz-Zanzi ◽  
Matteo Convertino
Keyword(s):  
Literature Review ◽  
South America ◽  
Yellow Fever ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Case Fatality ◽  
Surveillance Data ◽  
Fatality Risk ◽  
Disease Case ◽  
Case Fatality Risk

Abstract Background Case fatality risk (CFR), commonly referred to as a case fatality ratio or rate, represents the probability of a disease case being fatal. It is often estimated for various diseases through analysis of surveillance data, case reports, or record examinations. Reported CFR values for Yellow Fever vary, offering wide ranges. Estimates have not been found through systematic literature review, which has been used to estimate CFR of other diseases. This study aims to estimate the case fatality risk of severe Yellow Fever cases through a systematic literature review and meta-analysis. Methods A search strategy was implemented in PubMed and Ovid Medline in June 2019 and updated in March 2021, seeking reported severe case counts, defined by fever and either jaundice or hemorrhaging, and the number of those that were fatal. The searches yielded 1,133 studies, and title/abstract review followed by full text review produced 14 articles reporting 32 proportions of fatal cases, 26 of which were suitable for meta-analysis. Four studies with one proportion each were added to include clinical case data from the recent outbreak in Brazil. Data were analyzed through an intercept-only logistic meta-regression with random effects for study. Values of the I2 statistic measured heterogeneity across studies. Results The estimated CFR was 39 % (95 % CI: 31 %, 47 %). Stratifying by continent showed that South America observed a higher CFR than Africa, though fewer studies reported estimates for South America. No difference was seen between studies reporting surveillance data and studies investigating outbreaks, and no difference was seen among different symptom definitions. High heterogeneity was observed across studies. Conclusions Approximately 39 % of severe Yellow Fever cases are estimated to be fatal. This study provides the first systematic literature review to estimate the CFR of Yellow Fever, which can provide insight into outbreak preparedness and estimating underreporting.

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