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Schauer, Jacob M.; Lee, Jihyun; Diaz, Karina; Pigott, Therese D. – Research Synthesis Methods, 2022
Missing covariates is a common issue when fitting meta-regression models. Standard practice for handling missing covariates tends to involve one of two approaches. In a complete-case analysis, effect sizes for which relevant covariates are missing are omitted from model estimation. Alternatively, researchers have employed the so-called…
Descriptors: Statistical Bias, Meta Analysis, Regression (Statistics), Research Problems
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Mavridis, Dimitris; White, Ian R. – Research Synthesis Methods, 2020
Missing data result in less precise and possibly biased effect estimates in single studies. Bias arising from studies with incomplete outcome data is naturally propagated in a meta-analysis. Conventional analysis using only individuals with available data is adequate when the meta-analyst can be confident that the data are missing at random (MAR)…
Descriptors: Meta Analysis, Data Analysis, Statistical Bias, Outcome Measures
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Nejstgaard, Camilla Hansen; Lundh, Andreas; Abdi, Suhayb; Clayton, Gemma; Gelle, Mustafe Hassan Adan; Laursen, David Ruben Teindl; Olorisade, Babatunde Kazeem; Savovic, Jelena; Hróbjartsson, Asbjørn – Research Synthesis Methods, 2022
Randomised trials are often funded by commercial companies and methodological studies support a widely held suspicion that commercial funding may influence trial results and conclusions. However, these studies often have a risk of confounding and reporting bias. The risk of confounding is markedly reduced in meta-epidemiological studies that…
Descriptors: Medical Research, Randomized Controlled Trials, Corporations, Financial Support
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Lin, Lifeng; Chu, Haitao – Research Synthesis Methods, 2018
In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of…
Descriptors: Bayesian Statistics, Multivariate Analysis, Meta Analysis, Correlation
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Moustgaard, Helene; Jones, Hayley E.; Savovic, Jelena; Clayton, Gemma L.; Sterne, Jonathan AC; Higgins, Julian PT; Hróbjartsson, Asbjørn – Research Synthesis Methods, 2020
Randomized clinical trials underpin evidence-based clinical practice, but flaws in their conduct may lead to biased estimates of intervention effects and hence invalid treatment recommendations. The main approach to the empirical study of bias is to collate a number of meta-analyses and, within each, compare the results of trials with and without…
Descriptors: Epidemiology, Evidence, Medical Research, Intervention
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Yoneoka, Daisuke; Henmi, Masayuki – Research Synthesis Methods, 2017
Recently, the number of regression models has dramatically increased in several academic fields. However, within the context of meta-analysis, synthesis methods for such models have not been developed in a commensurate trend. One of the difficulties hindering the development is the disparity in sets of covariates among literature models. If the…
Descriptors: Meta Analysis, Multivariate Analysis, Research Problems, Regression (Statistics)