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Stephan B. Bruns; Teshome K. Deressa; T. D. Stanley; Chris Doucouliagos; John P. A. Ioannidis – Research Synthesis Methods, 2024
Using a sample of 70,399 published p-values from 192 meta-analyses, we empirically estimate the counterfactual distribution of p-values in the absence of any biases. Comparing observed p-values with counterfactually expected p-values allows us to estimate how many p-values are published as being statistically significant when they should have been…
Descriptors: Meta Analysis, Research Reports, Research Design, Microeconomics
Rebecca Whittle; Joie Ensor; Miriam Hattle; Paula Dhiman; Gary S. Collins; Richard D. Riley – Research Synthesis Methods, 2024
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a…
Descriptors: Data, Individual Characteristics, Participant Characteristics, Meta Analysis
Shimonovich, Michal; Pearce, Anna; Thomson, Hilary; Katikireddi, Srinivasa Vittal – Research Synthesis Methods, 2022
In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no…
Descriptors: Evidence, Synthesis, Causal Models, Public Health
Bowden, Jack; Holmes, Michael V. – Research Synthesis Methods, 2019
Mendelian randomization (MR) uses genetic variants as instrumental variables to infer whether a risk factor causally affects a health outcome. Meta-analysis has been used historically in MR to combine results from separate epidemiological studies, with each study using a small but select group of genetic variants. In recent years, it has been used…
Descriptors: Meta Analysis, Research Design, Predictor Variables, Genetics
Senior, Alistair M.; Viechtbauer, Wolfgang; Nakagawa, Shinichi – Research Synthesis Methods, 2020
Meta-analyses are often used to estimate the relative average values of a quantitative outcome in two groups (eg, control and experimental groups). However, they may also examine the relative variability (variance) of those groups. For such comparisons, two relatively new effect size statistics, the log-transformed "variability ratio"…
Descriptors: Meta Analysis, Effect Size, Research Design, Simulation
Brunner, Martin; Keller, Lena; Stallasch, Sophie E.; Kretschmann, Julia; Hasl, Andrea; Preckel, Franzis; Lüdtke, Oliver; Hedges, Larry V. – Research Synthesis Methods, 2023
Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational…
Descriptors: Meta Analysis, Surveys, Research Design, Educational Research
Hong, Sanghyun; Reed, W. Robert – Research Synthesis Methods, 2021
The purpose of this study is to show how Monte Carlo analysis of meta-analytic estimators can be used to select estimators for specific research situations. Our analysis conducts 1620 individual experiments, where each experiment is defined by a unique combination of sample size, effect size, effect size heterogeneity, publication selection…
Descriptors: Monte Carlo Methods, Meta Analysis, Research Methodology, Experiments
López-López, José A.; Page, Matthew J.; Lipsey, Mark W.; Higgins, Julian P. T. – Research Synthesis Methods, 2018
Systematic reviews often encounter primary studies that report multiple effect sizes based on data from the same participants. These have the potential to introduce statistical dependency into the meta-analytic data set. In this paper, we provide a tutorial on dealing with effect size multiplicity within studies in the context of meta-analyses of…
Descriptors: Effect Size, Literature Reviews, Meta Analysis, Research Methodology
Curtin, François – Research Synthesis Methods, 2017
Clinical trials have different designs: In late stage drug development, the parallel trial design is the most frequent one; however, the crossover design is not rare; different techniques are used to analyse their results. Although both designs measure the same treatment effect, combining parallel and crossover trials in a meta-analysis is not…
Descriptors: Meta Analysis, Computation, Research Design, Drug Therapy
Su, Yu-Xuan; Tu, Yu-Kang – Research Synthesis Methods, 2018
Network meta-analysis compares multiple treatments in terms of their efficacy and harm by including evidence from randomized controlled trials. Most clinical trials use parallel design, where patients are randomly allocated to different treatments and receive only 1 treatment. However, some trials use within person designs such as split-body,…
Descriptors: Network Analysis, Meta Analysis, Randomized Controlled Trials, Research Design
Langan, Dean; Higgins, Julian P. T.; Jackson, Dan; Bowden, Jack; Veroniki, Areti Angeliki; Kontopantelis, Evangelos; Viechtbauer, Wolfgang; Simmonds, Mark – Research Synthesis Methods, 2019
Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance,…
Descriptors: Simulation, Meta Analysis, Health, Comparative Analysis
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G. – Research Synthesis Methods, 2015
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…
Descriptors: Bayesian Statistics, Meta Analysis, Prediction, Nonparametric Statistics
Debray, Thomas P. A.; Moons, Karel G. M.; van Valkenhoef, Gert; Efthimiou, Orestis; Hummel, Noemi; Groenwold, Rolf H. H.; Reitsma, Johannes B. – Research Synthesis Methods, 2015
Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research…
Descriptors: Meta Analysis, Outcomes of Treatment, Research Methodology, Literature Reviews
Higgins, Julian P. T.; Lane, Peter W.; Anagnostelis, Betsy; Anzures-Cabrera, Judith; Baker, Nigel F.; Cappelleri, Joseph C.; Haughie, Scott; Hollis, Sally; Lewis, Steff C.; Moneuse, Patrick; Whitehead, Anne – Research Synthesis Methods, 2013
Background: Because meta-analyses are increasingly prevalent and cited in the medical literature, it is important that tools are available to assess their methodological quality. When performing an empirical study of the quality of published meta-analyses, we found that existing tools did not place a strong emphasis on statistical and…
Descriptors: Meta Analysis, Research Methodology, Quality Control, Measurement Techniques
Reeves, Barnaby C.; Higgins, Julian P. T.; Ramsay, Craig; Shea, Beverley; Tugwell, Peter; Wells, George A. – Research Synthesis Methods, 2013
Background: Methods need to be further developed to include non-randomised studies (NRS) in systematic reviews of the effects of health care interventions. NRS are often required to answer questions about harms and interventions for which evidence from randomised controlled trials (RCTs) is not available. Methods used to review randomised…
Descriptors: Research Methodology, Research Design, Health Services, Workshops
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