Bayesian Random-Effects Meta-Analysis Models in Gene Expression Studies
Random-effects meta-analysis models are commonly applied in combining effect sizes from individual gene expression studies. However, study heterogeneity is unknown and may arise from a variation of sample quality and experimental conditions. High heterogeneity of effect sizes can reduce the statisti...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2022
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/80974 |
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| Sumario: | Random-effects meta-analysis models are commonly applied in combining effect sizes from individual gene expression studies. However, study heterogeneity is unknown and may arise from a variation of sample quality and experimental conditions. High heterogeneity of effect sizes can reduce the statistical power of the models. In addition, classical random-effects meta-analysis models are based on a normal approximation, which may be limited to small samples and its results may be biased toward the null value. A Bayesian approach was used to avoid the approximation and the biases. We applied a sample-quality weight to adjust the study heterogeneity in the Bayesian random-effects meta-analysis model with weighted between-study variance on a sample quality indicator and illustrated the application of this approach in Alzheimer’s gene expression studies. |
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| ISBN: | 978-1-80355-621-5 |
| DOI: | 10.5772/intechopen.103124 |