Reader reaction on the fast small ‐sample kernel independence test for microbiome community‐level association analysis
Summary
Zhan et al. () presented a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition, and showed its competitive performance compared to existing methods. In this article, we clarify the close relation of KRV to the existing generalized RV (GRV) coefficient, and show that KRV and GRV have very similar performance. Although the KRV test could control the type I error rate well at 1% and 5% levels, we show that it could largely underestimate p‐values at small significance levels leading to significantly inflated type I errors. As a partial remedy, w...
Source: Biometrics - November 29, 2017 Category: Biotechnology Authors: Bin Guo, Baolin Wu Tags: READER REACTION Source Type: research
Discussion of “quantifying publication bias in meta‐analysis” by Lin et al.
Biometrics,Volume 74, Issue 3, Page 797-799, September 2018. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Authors: Christopher H. Schmid Source Type: research
Discussion on Quantifying publication bias in meta ‐analysis
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
Discussion of “quantifying publication bias in meta‐analysis” by Liu et al.
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
Rejoinder to “quantifying publication bias in meta‐analysis”
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
Quantifying publication bias in meta ‐analysis
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
Discussion on “Quantifying Publication Bias in Meta‐Analysis” by Lin and Chu
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
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Biometrics, Ahead of Print. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Source Type: research
Quantifying publication bias in meta ‐analysis
This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias. This measure describes the asymmetry of the collected studies’ distribution. In addition, a new test for publication bias is derived based on the skewness. Large sample properties of the new measure are studied, and its performance is illustrated using simulations and three case studies. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Authors: Lifeng Lin, Haitao Chu Tags: ORIGINAL ARTICLE Source Type: research
Discussion on Quantifying publication bias in meta ‐analysis
Abstract
In this discussion, I will describe some issues that are related to the article presented by Lin and Chu. In particular, I discuss three concerns that should be addressed before their methodology may be accepted for general use. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Authors: Dan Jackson Tags: DISCUSSION Source Type: research
Discussion of “quantifying publication bias in meta‐analysis” by Liu et al.
Summary
Inspection and analysis of funnel plots cannot reliably identify publication and reporting bias, the non‐publication of results that are not statistically significant. Instead, researchers should thoroughly and systematically search available information sources such as databases, registries and unpublished reports. Even then, it is not possible to ever know whether a systematic review has uncovered all available studies, but the search can inform attempts to construct plausible statistical models of the missing data mechanism. (Source: Biometrics)
Source: Biometrics - November 15, 2017 Category: Biotechnology Authors: Christopher H. Schmid Tags: DISCUSSION Source Type: research
