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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Bonferroni Test01:10

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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P-value01:10

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P-value is one of the most crucial concepts in statistics.
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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Sign Test for Matched Pairs01:17

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Powerful p-value combination methods to detect incomplete association.

Sora Yoon1, Bukyung Baik1, Taesung Park2,3

  • 1Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

Scientific Reports
|March 27, 2021
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Summary
This summary is machine-generated.

New meta-analysis methods, weighted Fisher (wFisher) and ordmeta, maintain statistical power even with unassociated data. These robust approaches outperform conventional methods, especially when few studies show true associations.

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Meta-analyses combine study statistics to enhance statistical power.
  • Conventional methods assume all data are associated with the phenotype, which is often not true due to experimental conditions or genetic heterogeneity.
  • Unassociated statistics, derived from the null distribution, can reduce the power of standard meta-analysis techniques.

Purpose of the Study:

  • To evaluate the performance of meta-analysis methods in the presence of unassociated statistics.
  • To develop and propose novel meta-analysis methods robust to unassociated data.
  • To compare the power and biological relevance of proposed methods against existing techniques.

Main Methods:

  • Evaluated conventional meta-analysis methods under varying proportions of unassociated statistics.
  • Assessed the classical Fisher's method and its weighted variant (wFisher).
  • Proposed and evaluated a new method, ordmeta, based on the joint distribution of ordered p-values.
  • Conducted simulation analyses using t-test, RNA-seq, and microarray data.
  • Performed meta-analyses on prostate cancer microarray datasets and body mass index association summary datasets.

Main Results:

  • Conventional meta-analysis power significantly decreases with increasing unassociated statistics.
  • Fisher's method and wFisher demonstrate robust power, performing well even with unassociated data.
  • wFisher and ordmeta outperformed existing methods in simulations when only a few studies had associations.
  • Applied methods identified biologically relevant genes missed by state-of-the-art techniques in real-world datasets.

Conclusions:

  • The presence of unassociated statistics significantly impacts meta-analysis power.
  • Weighted Fisher (wFisher) and ordmeta are robust and powerful meta-analysis methods, particularly in scenarios with limited true associations.
  • These novel methods offer improved biological relevance and discovery potential for genetic association studies.