GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications
| Autor: | Seongyong Park, Shujaat Khan, Ubaid M. Al-Saggaf, Muhammad Moinuddin |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0303 health sciences Information Theory (cs.IT) media_common.quotation_subject Computer Science - Information Theory Sample (statistics) 02 engineering and technology 021001 nanoscience & nanotechnology Measure (mathematics) Statistics - Applications Quantitative Biology - Quantitative Methods 03 medical and health sciences Strictly standardized mean difference Robustness (computer science) FOS: Biological sciences Statistics Applications (stat.AP) Metric (unit) Hit selection 0210 nano-technology Quantitative Methods (q-bio.QM) Normality 030304 developmental biology media_common Interpretability |
| Zdroj: | BIBM |
| Popis: | In many biological applications, the primary objective of study is to quantify the magnitude of treatment effect between two groups. Cohens'd or strictly standardized mean difference (SSMD) can be used to measure effect size however, it is sensitive to violation of assumption of normality. Here, we propose an alternative metric of standardized effect size measure to improve robustness and interpretability, based on the overlap between two sample distributions. The proposed method is a non-parametric generalized variant of SSMD (Strictly Standardized Mean Difference). We characterized proposed measure in various simulation settings to illustrate its behavior. We also investigated finite sample properties on the estimation of effect size and draw some guidelines. As a case study, we applied our measure for hit selection problem in an RNAi experiment and showed superiority of proposed method. Accepted in International Conference on Bioinformatics and Biomedicine (BIBM) 2020. arXiv admin note: text overlap with arXiv:2001.06384 |
| Databáze: | OpenAIRE |
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