scholarly journals A Simple and Robust Way of Concluding Meta-Analysis Results Using Reported P values, Standardized Effect Sizes, or Other Statistics

2012 ◽  
Vol 10 (4) ◽  
pp. 219-223 ◽  
Author(s):  
P.-H. Chyou
Keyword(s):  
Meta Analysis ◽  
Effect Sizes ◽  
P Values ◽  
Standardized Effect Sizes

Confidence Intervals

2009 ◽  
Vol 217 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Geoff Cumming ◽  
Fiona Fidler
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Prediction Intervals ◽  
Effect Sizes ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Standardized Effect Sizes ◽  
Mainstream Science

Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions – those that demand quantitative answers – and using CIs to answer them. They explain CIs and a range of ways to think about them and use them to interpret data, especially by considering CIs as prediction intervals, which provide information about replication. They explain how to calculate CIs on means, proportions, correlations, and standardized effect sizes, and illustrate symmetric and asymmetric CIs. They also argue that information provided by CIs is more useful than that provided by p values, or by values of Killeen’s prep, the probability of replication.

scholarly journals Role of Meta-analysis in Interpreting the Scientific Literature

2013 ◽  
Author(s):  
Michael D. Jennions ◽  
Christopher J. Lortie ◽  
Julia Koricheva
Keyword(s):  
Statistical Power ◽  
Scientific Literature ◽  
Meta Analysis ◽  
Effect Sizes ◽  
P Values ◽  
Single Study ◽  
Broad Scale

This chapter begins with a brief review of why effect sizes and their variances are more informative than P-values. It then discusses how meta-analysis promotes “effective thinking” that can change approaches to several commonplace problems. Specifically, it addresses the issues of (1) exemplar studies versus average trends, (2) resolving “conflict” between specific studies, (3) presenting results, (4) deciding on the level at which to replicate studies, (5) understanding the constraints imposed by low statistical power, and (6) asking broad-scale questions that cannot be resolved in a single study. The chapter focuses on estimating effect sizes as a key outcome of meta-analysis, but acknowledges that other outcomes might be of more interest in other situations.

scholarly journals Peto odds ratios demonstrate no advantage over classic odds ratios in meta-analysis of binary rare outcomes

2020 ◽  
Author(s):  
Chang Xu ◽  
Luis Furuya-Kanamori ◽  
Lifeng Lin ◽  
Suhail A. Doi
Keyword(s):  
Event Rate ◽  
Meta Analysis ◽  
Rare Event ◽  
Effect Sizes ◽  
Binary Outcomes ◽  
Odds Ratios ◽  
P Values ◽  
Cochrane Database ◽  
Meta Analyses ◽  
The Impact

AbstractIn this study, we examined the discrepancy between large studies and small studies in meta-analyses of rare event outcomes and the impact of Peto versus the classic odds ratios (ORs) through empirical data from the Cochrane Database of Systematic Reviews that collected from January 2003 to May 2018. Meta-analyses of binary outcomes with rare events (event rate ≤5%), with at least 5 studies, and with at least one large study (N≥1000) were extracted. The Peto and classic ORs were used as the effect sizes in the meta-analyses, and the magnitude and direction of the ORs of the meta-analyses of large studies versus small studies were compared. The p-values of the meta-analyses of small studies were examined to assess if the Peto and the classic OR methods gave similar results. Totally, 214 meta-analyses were included. Over the total 214 pairs of pooled ORs of large studies versus pooled small studies, 66 (30.84%) had a discordant direction (kappa=0.33) when measured by Peto OR and 69 (32.24%) had a discordant direction (kappa=0.22) when measured by classic OR. The Peto ORs resulted in smaller p-values compared to classic ORs in a substantial (83.18%) number of cases. In conclusion, there is considerable discrepancy between large studies and small studies among the results of meta-analyses of sparse data. The use of Peto odds ratios does not improve this situation and is not recommended as it may result in less conservative error estimation.

scholarly journals Enhancing the interpretation of statistical P values in toxicology studies: implementation of linear mixed models (LMMs) and standardized effect sizes (SESs)

2015 ◽  
Vol 90 (3) ◽  
pp. 731-751 ◽  
Author(s):  
Kerstin Schmidt ◽  
Jörg Schmidtke ◽  
Christian Kohl ◽  
Ralf Wilhelm ◽  
Joachim Schiemann ◽  
...  
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Effect Sizes ◽  
P Values ◽  
Standardized Effect Sizes

scholarly journals Statistical reporting of metabolomics data: experience from a high-throughput NMR platform and epidemiological applications

Metabolomics ◽  
2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Stefan Mutter ◽  
Carrie Worden ◽  
Kara Paxton ◽  
Ville-Petteri Mäkinen
Keyword(s):  
Literature Review ◽  
High Throughput ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Metabolomics Data ◽  
P Values ◽  
Reporting Practices ◽  
Numeric Formats ◽  
Meta Analyses

Abstract Introduction Meta-analysis is the cornerstone of robust biomedical evidence. Objectives We investigated whether statistical reporting practices facilitate metabolomics meta-analyses. Methods A literature review of 44 studies that used a comparable platform. Results Non-numeric formats were used in 31 studies. In half of the studies, less than a third of all measures were reported. Unadjusted P-values were missing from 12 studies and exact P-values from 9 studies. Conclusion  Reporting practices can be improved. We recommend (i) publishing all results as numbers, (ii) reporting effect sizes of all measured metabolites and (iii) always reporting unadjusted exact P-values.

A systematic review and meta-analysis of set-shifting ability in eating disorders

2007 ◽  
Vol 37 (8) ◽  
pp. 1075-1084 ◽  
Author(s):  
MARION E. ROBERTS ◽  
KATE TCHANTURIA ◽  
DANIEL STAHL ◽  
LAURA SOUTHGATE ◽  
JANET TREASURE
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
Meta Analysis ◽  
Neuropsychological Test ◽  
Effect Sizes ◽  
Outcome Variable ◽  
Wisconsin Card Sort Test ◽  
Set Shifting ◽  
Standardized Effect Sizes ◽  
Neuropsychological Tasks

ABSTRACTBackgroundThe aim was to critically appraise and synthesize the literature relating to set-shifting ability in eating disorders. PsycINFO, Medline, and Web of Science databases were searched to December 2005. Hand searching of eating-disorder journals and relevant reference sections was also undertaken.MethodThe 15 selected studies contained both eating disorder and healthy control groups, and employed at least one of the following six neuropsychological measures of set-shifting ability; Trail Making Test (TMT), Wisconsin Card Sort Test (WCST), Brixton task, Haptic Illusion, CatBat task, or the set-shifting subset of the Cambridge Neuropsychological Test Automated Battery (CANTAB). The outcome variable was performance on the set-shifting aspect of the task. Pooled standardized mean differences (effect sizes) were calculated.ResultsTMT, WCST, CatBat and Haptic tasks had sufficient sample sizes for meta-analysis. These four tasks yielded acceptable pooled standardized effect sizes (0·36; TMT −1·05; Haptic) with moderate variation within studies (as measured by confidence intervals). The Brixton task showed a small pooled mean difference, and displayed more variation between sample results. The effect size for CANTAB set shifting was 0·17.ConclusionProblems in set shifting as measured by a variety of neuropsychological tasks are present in people with eating disorders.

scholarly journals Coinfections with Bacteria, Fungi, and Respiratory Viruses in Patients with SARS-CoV-2: A Systematic Review and Meta-Analysis

Pathogens ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 809
Author(s):  
Saad Alhumaid ◽  
Abbas Al Mutair ◽  
Zainab Al Alawi ◽  
Abeer M. Alshawi ◽  
Salamah A. Alomran ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cohort Studies ◽  
Visual Inspection ◽  
Fungal Infections ◽  
Meta Analysis ◽  
Case Series ◽  
Respiratory Viruses ◽  
Effect Sizes ◽  
P Values ◽  
Funnel Plots

Background: Coinfection with bacteria, fungi, and respiratory viruses in SARS-CoV-2 is of particular importance due to the possibility of increased morbidity and mortality. In this meta-analysis, we calculated the prevalence of such coinfections. Methods: Electronic databases were searched from 1 December 2019 to 31 March 2021. Effect sizes of prevalence were pooled with 95% confidence intervals (CIs). To minimize heterogeneity, we performed sub-group analyses. Results: Of the 6189 papers that were identified, 72 articles were included in the systematic review (40 case series and 32 cohort studies) and 68 articles (38 case series and 30 cohort studies) were included in the meta-analysis. Of the 31,953 SARS-CoV-2 patients included in the meta-analysis, the overall pooled proportion who had a laboratory-confirmed bacterial infection was 15.9% (95% CI 13.6–18.2, n = 1940, 49 studies, I2 = 99%, p < 0.00001), while 3.7% (95% CI 2.6–4.8, n = 177, 16 studies, I2 = 93%, p < 0.00001) had fungal infections and 6.6% (95% CI 5.5–7.6, n = 737, 44 studies, I2 = 96%, p < 0.00001) had other respiratory viruses. SARS-CoV-2 patients in the ICU had higher co-infections compared to ICU and non-ICU patients as follows: bacterial (22.2%, 95% CI 16.1–28.4, I2 = 88% versus 14.8%, 95% CI 12.4–17.3, I2 = 99%), and fungal (9.6%, 95% CI 6.8–12.4, I2 = 74% versus 2.7%, 95% CI 0.0–3.8, I2 = 95%); however, there was an identical other respiratory viral co-infection proportion between all SARS-CoV-2 patients [(ICU and non-ICU) and the ICU only] (6.6%, 95% CI 0.0–11.3, I2 = 58% versus 6.6%, 95% CI 5.5–7.7, I2 = 96%). Funnel plots for possible publication bias for the pooled effect sizes of the prevalence of coinfections was asymmetrical on visual inspection, and Egger’s tests confirmed asymmetry (p values < 0.05). Conclusion: Bacterial co-infection is relatively high in hospitalized patients with SARS-CoV-2, with little evidence of S. aureus playing a major role. Knowledge of the prevalence and type of co-infections in SARS-CoV-2 patients may have diagnostic and management implications.

scholarly journals Understanding tree failure—A systematic review and meta-analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246805
Author(s):  
Marinus van Haaften ◽  
Yili Liu ◽  
Yuxin Wang ◽  
Yueyue Zhang ◽  
Cornelis Gardebroek ◽  
...  
Keyword(s):  
Bayes Factor ◽  
Meta Analysis ◽  
Urban Trees ◽  
Effect Sizes ◽  
Physical Factors ◽  
P Values ◽  
Different Types ◽  
Funnel Plots ◽  
Regression Tests ◽  
Stem Failure

Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.

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