Unrestricted weighted least squares represent medical research better than random effects in 67,308 Cochrane meta-analyses

Stanley, T. D.; Ioannidis, John P. A.; Maier, Maximilian; Doucouliagos, Hristos; Otte, Willem M.; Bartoš, František

doi:10.1016/j.jclinepi.2023.03.004

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…[14][15][16] Recently, UWLS is shown to better represent medical research than RE across over 67,000 meta-analyses of approximately 600,000 studies. 20 iii We also simulate more complex multiple regression with 4, 6, and 10 independent variables. Results from these more complex multiple regressions are practically equivalent and are reported below and in the Supplement.…”

Section: Author Contributionsmentioning

confidence: 99%

“…ii ii The unrestricted weighted least squares (UWLS) weighted average has been shown to have better statistical properties than RE when there is publication selection bias or when heterogeneity is correlated with sample size (or SE), which meta‐research evidence finds in psychology 14‐16 . Recently, UWLS is shown to better represent medical research than RE across over 67,000 meta‐analyses of approximately 600,000 studies 20 …”

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confidence: 99%

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Meta‐analyses of partial correlations are biased: Detection and solutions

Stanley,

Doucouliagos,

Havranek

2024

Research Synthesis Methods

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We demonstrate that all meta‐analyses of partial correlations are biased, and yet hundreds of meta‐analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS+3. UWLS+3 is the unrestricted weighted least squares weighted average that makes an adjustment to the degrees of freedom that are used to calculate partial correlations and, by doing so, renders trivial any remaining meta‐analysis bias. Our simulations also reveal that these meta‐analysis biases are small‐sample biases (n < 200), and a simple correction factor of (n − 2)/(n − 1) greatly reduces these small‐sample biases along with Fisher's z. In many applications where primary studies typically have hundreds or more observations, partial correlations can be meta‐analyzed in standard ways with only negligible bias. However, in other fields in the social and the medical sciences that are dominated by small samples, these meta‐analysis biases are easily avoidable by our proposed methods.

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Section: Author Contributionsmentioning

confidence: 99%

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confidence: 99%

Meta‐analyses of partial correlations are biased: Detection and solutions

Stanley,

Doucouliagos,

Havranek

2024

Research Synthesis Methods

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“…In multiple papers, Stanley and Doucoucliagos promote the superior performance of the UWLS. 6,7 For example, in a recent paper on medical research, they (along with coauthors) conclude: "UWLS frequently dominates RE in medical research, often substantially. Thus, the UWLS should be reported routinely in the meta-analysis of clinical trials."…”

Section: A Fairer Testmentioning

confidence: 99%

“…UWLS produces coefficient estimates that are identical to FE, but makes an adjustment to the standard errors. In multiple papers, Stanley and Doucoucliagos promote the superior performance of the UWLS 6,7 . For example, in a recent paper on medical research, they (along with co‐authors) conclude: “ UWLS frequently dominates RE in medical research, often substantially.…”

Section: A Fairer Testmentioning

confidence: 99%

“…For example, in a recent paper on medical research, they (along with co‐authors) conclude: “ UWLS frequently dominates RE in medical research, often substantially. Thus, the UWLS should be reported routinely in the meta‐analysis of clinical trials .” 6 Accordingly, our additional simulations investigate the performance of the UWLS estimator. It should perform well in Case 2 when there is heteroskedasticity and effect homogeneity, since these are the conditions for which the FE estimator was developed 8 .…”

Section: A Fairer Testmentioning

confidence: 99%

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Meta‐analysis and partial correlation coefficients: A matter of weights

Hong,

Reed

2023

Research Synthesis Methods

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This study builds on the simulation framework of a recent paper by Stanley and Doucouliagos (Research Synthesis Methods 2023;14;515–519). S&D use simulations to make the argument that meta‐analyses using partial correlation coefficients (PCCs) should employ a “suboptimal” estimator of the PCC standard error when constructing weights for fixed effect and random effects estimation. We address concerns that their simulations and subsequent recommendation may give meta‐analysts a misleading impression. While the estimator they promote dominates the “correct” formula in their Monte Carlo framework, there are other estimators that perform even better. We conclude that more research is needed before best practice recommendations can be made for meta‐analyses with PCCs.

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