Controlling the risk of spurious findings from meta‐regression

By Julian P. T. Higgins, S. Thompson. Statistics in Medicine, 2004

1,256 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/980615310fb33e61a80f3cc6e4c946bc95b8f154

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Citations in the corpus

Peripheral cytokine and chemokine alterations in depression: a meta‐analysis of 82 studies (2017, methodology): “Meta-regression analyses were conducted only when at least 10 studies provided moderator; this decision was made a priori because with fewer datasets, this analytic tool may provide spurious results (33).”
A re‐evaluation of fixed effect(s) meta‐analysis (2018, background): “…through weighted linear regression and is SE.β̂MR/= √{ 1 / k∑ i=1 wi.xi − x̂/2 } : .6/ When true linearity of the βi with xi does not hold, this is not in general a valid estimate of the variability of β̂MR, and statistical inference based on it will not be valid (Higgins and Thompson, 2004).”
Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis (2016, methodology): “performing meta-regression with multiple covariates, we used the model F value and its statistical significance to assess whether there was evidence for an association of any of the covariates with the outcome, and a test based on random permutations to calculate multiplicity adjusted P value [29,30”
The Value of Housing Characteristics: A Meta Analysis (2006, neutral): “ Higgins and Thompson (2004) assert that a fixed-effects meta regression reduces the risk of false positive results and they recommend using of a random effects model.”
Extending DerSimonian and Laird's methodology to perform multivariate random effects meta‐analyses (2009, methodology): “Typically, relatively simple procedures are used when combining the results, although more sophisticated methodology is also available [1]; for example, meta-regression [2, 3] provides the means to investigate the associations of study-level covariates with the treatment effect and explain between-s”
Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression (2020, methodology): “Finally, we investigated the impact of physical distancing measures on depression and anxiety through meta-regression models (Higgins & Thompson, 2004).”
Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program. (2011, methodology): “Use of permutation test for meta-regression can help assess the level of statistical significance of an observed meta-regression finding [52].”
The Impact of Railway Stations on Residential and Commercial Property Value: A Meta-analysis (2007, neutral): “In such a situation Higgins and Thompson (2004) have indicated that fixed effect meta-regression models suffer from false positive results compared to the conventional regression model.”
Aid Effectiveness on Growth. A Meta Study (2008, background): “An increasingly popular test in meta-analysis is the permutation test (Higgins and Thompson 2004).”