scholarly journals A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 267
Author(s):  
Nanami Taketomi ◽  
Yoshihiko Konno ◽  
Yuan-Tsung Chang ◽  
Takeshi Emura
Keyword(s):  
Meta Analysis ◽  
Isotonic Regression ◽  
Simultaneous Estimation ◽  
Shrinkage Estimators ◽  
Common Mean ◽  
Stein Shrinkage ◽  
The Common ◽  
The Individual ◽  
Gastric Cancer Patients ◽  
Meta Analyses

Meta-analyses combine the estimators of individual means to estimate the common mean of a population. However, the common mean could be undefined or uninformative in some scenarios where individual means are “ordered” or “sparse”. Hence, assessments of individual means become relevant, rather than the common mean. In this article, we propose simultaneous estimation of individual means using the James–Stein shrinkage estimators, which improve upon individual studies’ estimators. We also propose isotonic regression estimators for ordered means, and pretest estimators for sparse means. We provide theoretical explanations and simulation results demonstrating the superiority of the proposed estimators over the individual studies’ estimators. The proposed methods are illustrated by two datasets: one comes from gastric cancer patients and the other from COVID-19 patients.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Large-scale randomized evidence: Trials and meta-analyses of trials

2020 ◽  
pp. 51-66
Author(s):  
Colin Baigent ◽  
Richard Peto ◽  
Richard Gray ◽  
Natalie Staplin ◽  
Sarah Parish ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Large Scale ◽  
Meta Analysis ◽  
False Negative ◽  
Premature Deaths ◽  
Common Causes ◽  
The Common ◽  
Meta Analyses ◽  
Positive Results

Clinical trials generally need to be able to detect or to refute realistically moderate (but still worthwhile) differences between treatments in long-term disease outcome. Large-scale randomized evidence should be able to detect such effects, but medium-sized trials or medium-sized meta-analyses can, and often do, yield false-negative or exaggeratedly positive results. Hundreds of thousands of premature deaths each year could be avoided by seeking appropriately large-scale randomized evidence about various widely practicable treatments for the common causes of death, and by disseminating this evidence appropriately. This chapter takes a look at the use of large-scale randomized evidence—produced from trials and meta-analysis of trials—and how this data should be handled in order to produce accurate result.

scholarly journals The Effect of Bilingualism on Older Adults’ Inhibitory Control: A Meta-Analysis

2019 ◽  
Author(s):  
Bonnie A Armstrong ◽  
Natalie Ein ◽  
Brenda I Wong ◽  
Sara N Gallant ◽  
Lingqian Li
Keyword(s):  
Older Adults ◽  
Second Language Acquisition ◽  
Inhibitory Control ◽  
Language Proficiency ◽  
Meta Analysis ◽  
Individual Characteristics ◽  
Bilingual Advantage ◽  
The Individual ◽  
Meta Analyses ◽  
Design And Methods

AbstractBackground and ObjectivesThe effect bilingualism has on older adults’ inhibitory control has been extensively investigated, yet there is continued controversy regarding whether older adult bilinguals show superior inhibitory control compared with monolinguals. The objective of the current meta-analysis was to examine the reliability and magnitude of the bilingualism effect on older adults’ inhibitory control as measured by the Simon and Stroop tasks. In addition, we examined whether individual characteristics moderate the bilingual advantage in inhibition, including age (young–old vs old–old), age of second language acquisition, immigrant status, language proficiency, and frequency of language use.Research Design and MethodsA total of 22 samples for the Simon task and 14 samples for the Stroop task were derived from 28 published and unpublished articles (32 independent samples, with 4 of these samples using more than 1 task) and were analyzed in 2 separate meta-analyses.ResultsAnalyses revealed a reliable effect of bilingualism on older adults’ performance on the Simon (g = 0.60) and Stroop (g = 0.27) tasks. Interestingly, individual characteristics did not moderate the association between bilingualism and older adults’ inhibitory control.Discussion and ImplicationsThe results suggest there is a bilingual advantage in inhibitory control for older bilinguals compared with older monolinguals, regardless of the individual characteristics previously thought to moderate this effect. Based on these findings, bilingualism may protect inhibitory control from normal cognitive decline with age.

scholarly journals A Meta-Analysis of 46 Studies Identified by the FDA Demonstrates that Soy Protein Decreases Circulating LDL and Total Cholesterol Concentrations in Adults

2019 ◽  
Vol 149 (6) ◽  
pp. 968-981 ◽  
Author(s):  
Sonia Blanco Mejia ◽  
Mark Messina ◽  
Siying S Li ◽  
Effie Viguiliouk ◽  
Laura Chiavaroli ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Soy Protein ◽  
Ldl Cholesterol ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Heart Health ◽  
Health Claim ◽  
The Individual ◽  
Dose Response Effect ◽  
Meta Analyses

ABSTRACT Background Certain plant foods (nuts and soy protein) and food components (viscous fibers and plant sterols) have been permitted by the FDA to carry a heart health claim based on their cholesterol-lowering ability. The FDA is currently considering revoking the heart health claim for soy protein due to a perceived lack of consistent LDL cholesterol reduction in randomized controlled trials. Objective We performed a meta-analysis of the 46 controlled trials on which the FDA will base its decision to revoke the heart health claim for soy protein. Methods We included the 46 trials on adult men and women, with baseline circulating LDL cholesterol concentrations ranging from 110 to 201 mg/dL, as identified by the FDA, that studied the effects of soy protein on LDL cholesterol and total cholesterol (TC) compared with non-soy protein. Two independent reviewers extracted relevant data. Data were pooled by the generic inverse variance method with a random effects model and expressed as mean differences with 95% CI. Heterogeneity was assessed and quantified. Results Of the 46 trials identified by the FDA, 43 provided data for meta-analyses. Of these, 41 provided data for LDL cholesterol, and all 43 provided data for TC. Soy protein at a median dose of 25 g/d during a median follow-up of 6 wk decreased LDL cholesterol by 4.76 mg/dL (95% CI: −6.71, −2.80 mg/dL, P < 0.0001; I2 = 55%, P < 0.0001) and decreased TC by 6.41 mg/dL (95% CI: −9.30, −3.52 mg/dL, P < 0.0001; I2 = 74%, P < 0.0001) compared with non-soy protein controls. There was no dose–response effect or evidence of publication bias for either outcome. Inspection of the individual trial estimates indicated most trials (∼75%) showed a reduction in LDL cholesterol (range: −0.77 to −58.60 mg/dL), although only a minority of these were individually statistically significant. Conclusions Soy protein significantly reduced LDL cholesterol by approximately 3–4% in adults. Our data support the advice given to the general public internationally to increase plant protein intake. This trial was registered at clinicaltrials.gov as NCT03468127.

scholarly journals Critical Essay: Meta-analysis: A critical realist critique and alternative

2016 ◽  
Vol 70 (1) ◽  
pp. 11-39 ◽  
Author(s):  
Matthew J Brannan ◽  
Steve Fleetwood ◽  
Joe O’Mahoney ◽  
Steve Vincent
Keyword(s):  
Quantitative Research ◽  
Meta Analysis ◽  
Explanatory Power ◽  
Organization Studies ◽  
Critical Realist ◽  
Quantitative Studies ◽  
Potential Alternative ◽  
The Individual ◽  
Meta Analyses ◽  
Meta Theory

Meta-analysis has proved increasingly popular in management and organization studies as a way of combining existing empirical quantitative research to generate a statistical estimate of how strongly variables are associated. Whilst a number of studies identify technical, procedural and practical limitations of meta-analyses, none have yet tackled the meta-theoretical flaws in this approach. We deploy critical realist meta-theory to argue that the individual quantitative studies, upon which meta-analysis relies, lack explanatory power because they are rooted in quasi-empiricist meta-theory. This problem, we argue, is carried over in meta-analyses. We then propose a ‘critical realist synthesis’ as a potential alternative to the use of meta-analysis in organization studies and social science more widely.

scholarly journals History and Spread of Viruses (COVID-19) and Associated Demographic and Clinical Parameters – A systematic review and meta-analysis

2021 ◽  
Vol 33 (1) ◽  
pp. 9-24
Author(s):  
Swambhavi Awasthi ◽  
Sunil Sharma ◽  
Saurav Attri ◽  
Sakshi Malik Attri ◽  
Rajesh Sharawat ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Primary Objective ◽  
Asymptomatic Patients ◽  
Effect Model ◽  
Co Morbidity ◽  
The Common ◽  
Meta Analyses

COVID-19 made a huge impact on the world due to its rapid transmission and no treatments being available for it. The virus affected more people and spread to various countries than what was predicted when COVID-19 initially began spreading. There have been numerous pandemics and epidemics in the 21st century yet COVID-19 has affected more people and spread widely. The primary objective of the study was to explore history, spread and associated parameters of existing viruses especially COVID-19. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed for a systematic search to identify eligible published articles. Clinical data, regarding COVID-19 patients, was obtained from previously published articles. The main cause of COVID-19 spreading rapidly was noted to be due to a high percentage of asymptomatic patients, transmission being air-borne, and the lack of knowledge and preventative measures being implemented when the virus began spreading. The common co-morbidity that found in patients was Diabetes Mellitus, Hypertension, and Coronary Heart Disease. The common symptoms, found through the Meta-analysis, that the patients faced included cough (55.4%), fever (68.4%), fatigue (20.3%), and shortness of breath (18.1%). The proportion of asymptotic positive cases was measured 58.3% (95%CI: 24.7% – 87.9%) while mortality proportion was found to be 6.7% (fixed-effect model) and 13.4% (random-effect model). The Meta-analysis indicated that a higher percentage of males were affected by COVID-19 than females and more patients are found to be asymptomatic. Moreover, the mortality rate of patients that have had COVID-19 was found to be low. 

scholarly journals Methods used to meta-analyse results from interrupted time series studies: A methodological systematic review protocol

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 110
Author(s):  
Elizabeth Korevaar ◽  
Amalia Karahalios ◽  
Andrew B. Forbes ◽  
Simon L. Turner ◽  
Steve McDonald ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Meta Analysis ◽  
Interrupted Time Series ◽  
Analysis Methods ◽  
Study Selection ◽  
The Individual ◽  
Meta Analyses

Background: Systematic reviews are used to inform healthcare decision making. In reviews that aim to examine the effects of organisational, policy change or public health interventions, or exposures, evidence from interrupted time series (ITS) studies may be included. A core component of many systematic reviews is meta-analysis, which is the statistical synthesis of results across studies. There is currently a lack of guidance informing the choice of meta-analysis methods for combining results from ITS studies, and there have been no studies examining the meta-analysis methods used in practice. This study therefore aims to describe current meta-analysis methods used in a cohort of reviews of ITS studies. Methods: We will identify the 100 most recent reviews (published between 1 January 2000 and 11 October 2019) that include meta-analyses of ITS studies from a search of eight electronic databases covering several disciplines (public health, psychology, education, economics). Study selection will be undertaken independently by two authors. Data extraction will be undertaken by one author, and for a random sample of the reviews, two authors. From eligible reviews we will extract details at the review level including discipline, type of interruption and any tools used to assess the risk of bias / methodological quality of included ITS studies; at the meta-analytic level we will extract type of outcome, effect measure(s), meta-analytic methods, and any methods used to re-analyse the individual ITS studies. Descriptive statistics will be used to summarise the data. Conclusions: This review will describe the methods used to meta-analyse results from ITS studies. Results from this review will inform future methods research examining how different meta-analysis methods perform, and ultimately, the development of guidance.

scholarly journals Invited Commentary: The Disillusionment of Developmental Origins of Health and Disease (DOHaD) Epidemiology

2019 ◽  
Vol 189 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Stephen E Gilman ◽  
Mady Hornig
Keyword(s):  
Observational Studies ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Developmental Origins ◽  
Short Term ◽  
Hyperactivity Disorder ◽  
Health And Disease ◽  
The Individual ◽  
Meta Analyses

Abstract The developmental origins of health and disease (DOHaD) model promises a greater understanding of early development but has left unresolved the balance of risks and benefits to offspring of medication use during pregnancy. Masarwa et al. (Am J Epidemiol. 2018;187(8):1817–1827) conducted a meta-analysis of the association between in utero acetaminophen exposure and risks of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). A challenge of meta-analyzing results from observational studies is that summary measures of risk do not correspond to well-defined interventions when the individual studies adjusted for different covariate sets, which was the case here. This challenge limits the usefulness of observational meta-analyses for inferences about etiology and treatment planning. With that limitation understood, Masarwa et al. reported a 20%–30% higher risk of ADHD and ASD following prenatal acetaminophen exposure. Surprisingly, most of the original studies did not report diagnoses of ADHD or ASD. As a result, their summary estimates of risk are not informative about children’s likelihood of ADHD and ASD diagnoses. The long-term promise of DOHaD remains hopeful, but more effort is needed in the short-term to critically evaluate observational studies suggesting risks associated with medications used to treat conditions during pregnancy that might have adverse consequences for a developing fetus.

scholarly journals Enriching Meta-Analytic Models of Summary Data: A Thought Experiment and Case Study

2019 ◽  
Vol 3 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Blakeley B. McShane ◽  
Ulf Böckenholt
Keyword(s):  
Thought Experiment ◽  
Meta Analysis ◽  
Psychological Research ◽  
Individual Level ◽  
Level Data ◽  
The Common ◽  
The Individual ◽  
Analytic Models ◽  
Summary Data

Meta-analysis typically involves the analysis of summary data (e.g., means, standard deviations, and sample sizes) from a set of studies via a statistical model that is a special case of a hierarchical (or multilevel) model. Unfortunately, the common summary-data approach to meta-analysis used in psychological research is often employed in settings where the complexity of the data warrants alternative approaches. In this article, we propose a thought experiment that can lead meta-analysts to move away from the common summary-data approach to meta-analysis and toward richer and more appropriate summary-data approaches when the complexity of the data warrants it. Specifically, we propose that it can be extremely fruitful for meta-analysts to act as if they possess the individual-level data from the studies and consider what model specifications they might fit even when they possess only summary data. This thought experiment is justified because (a) the analysis of the individual-level data from the studies via a hierarchical model is considered the “gold standard” for meta-analysis and (b) for a wide variety of cases common in meta-analysis, the summary-data and individual-level-data approaches are, by a principle known as statistical sufficiency, equivalent when the underlying models are appropriately specified. We illustrate the value of our thought experiment via a case study that evolves across five parts that cover a wide variety of data settings common in meta-analysis.

Statistical Models for the Meta-analysis of Nonindependent Data

2013 ◽  
Author(s):  
Kerrie Mengersen ◽  
Michael D. Jennions ◽  
Christopher H. Schmid
Keyword(s):  
Statistical Models ◽  
Single Case ◽  
Meta Analysis ◽  
Dry Weight ◽  
Multiple Treatments ◽  
The Individual ◽  
Meta Analyses ◽  
Size Estimates ◽  
Over Time

In many meta-analyses, independence is questionable because there are several effect estimates per study and/or some of the individual studies included in the meta-analysis might not provide independent estimates of the effect. Within-study nonindependence can arise due to multiple measures of the same effect on the same experimental units being made over time, multiple treatments being compared to the same set of control individuals, or different measures being taken (e.g., plant height, dry weight, and photosynthesis rate) from the same experimental units to generate several different effect size estimates. This chapter discusses nonindependence among effect sizes both within and among studies. It focuses on four commonplace situations where nonindependence can occur in ecology and evolution meta-analyses. Each of these four situations is illustrated with a single case study.

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