Combining estimators in interlaboratory studies and meta‐analyses

Huang, Hening

doi:10.1002/jrsm.1633

Cited by 7 publications

(1 citation statement)

References 55 publications

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“…For the two-sample case, the GDE [6] showed first that an unbiased estimator has a smaller variance than either sample mean provided that both sample sizes are greater than 10. Since then, several papers have been written generalizing and extending their findings [7][8][9][10][11] and the references therein. On the other hand, Meier [2] suggested a method for setting an approximate confidence interval for µ centered at μGD .…”

Section: Introductionmentioning

confidence: 99%

Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations

Mphekgwana,

Kifle,

Marange

2024

Mathematics

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The challenge of combining two unbiased estimators is a common occurrence in applied statistics, with significant implications across diverse fields such as manufacturing quality control, medical research, and the social sciences. Despite the widespread relevance of estimating the common population mean μ, this task is not without its challenges. A particularly intricate issue arises when the variations within populations are unknown or possibly unequal. Conventional approaches, like the two-sample t-test, fall short in addressing this problem as they assume equal variances among the two populations. When there exists prior information regarding population variances (σi2,i=1,2), with the consideration that σ12 and σ22 might be equal, a hypothesis test can be conducted: H0:σ12=σ22 versus H1:σ12≠σ22. The initial sample is utilized to test H0, and if we fail to reject H0, we gain confidence in incorporating our prior knowledge (after testing) to estimate the common mean μ. However, if H0 is rejected, indicating unequal population variances, the prior knowledge is discarded. In such cases, a second sample is obtained to compensate for the loss of prior knowledge. The estimation of the common mean μ is then carried out using either the Graybill–Deal estimator (GDE) or the maximum likelihood estimator (MLE). A noteworthy discovery is that the proposed preliminary testimators, denoted as μ^PT1 and μ^PT2, exhibit superior performance compared to the widely used unbiased estimators (GDE and MLE).

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Section: Introductionmentioning

confidence: 99%

Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations

Mphekgwana,

Kifle,

Marange

2024

Mathematics

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Reference value of the JNdi-1 isotopic material without normalization

Quemet,

Lasnier,

Mialle

et al. 2024

J. Anal. At. Spectrom.

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Evolution of statistical models for meta-analysis and implications for best practice

Kelley

2023

Current Opinion in Epidemiology and Public Health

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Purpose of review Meta-analyses are a common and important component of clinical practice guidelines. Concomitantly, there has been a tremendous increase over the past three decades in the number of published meta-analyses. An important factor in the quality of the results from a meta-analysis rests on selecting the most appropriate pooling model. In this brief review, the evolution of the numerous different pooling models that extend beyond the traditional fixed effect, fixed effects, and random effects models is described, with a focus on estimating between-study variance, that is, heterogeneity. The most recent evidence, including alternative models, is also described and recommendations for model selection and reporting provided. Recent findings In the absence of checking for between-study normality, appropriately conducted simulation studies have found that the IVhet model, a quasi-likelihood approach, may be the best model for pooling results in an aggregate data meta-analysis. Summary The IVhet model is recommended for pooling results for an aggregate data meta-analysis. If there is insistence on a random effects model, the restricted maximum likelihood method along with the Knapp-Hartung adjustment is recommended. A need exists for a large, collaborative, appropriately conducted simulation study that examines which pooling models are best based on the scenario presented.

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Combining estimators in interlaboratory studies and meta‐analyses

Cited by 7 publications

References 55 publications

Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations

Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations

Reference value of the JNdi-1 isotopic material without normalization

Evolution of statistical models for meta-analysis and implications for best practice

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