GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications

Seongyong Park; Shujaat Khan; Muhammad Moinuddin; Ubaid M. Al-Saggaf

doi:10.1109/BIBM49941.2020.9313582

GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications

Seongyong Park
, Shujaat Khan
, Muhammad Moinuddin
, Ubaid M. Al-Saggaf

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In many biological applications, the primary objective of study is to quantity 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.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1096-1099
Number of pages	4
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313582
State	Published - 16 Dec 2020
Externally published	Yes

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Biological applications
Interpretability
Overlap statistics
Robustness
Standardized effect size measure

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management
Medicine (miscellaneous)
Health Informatics

Access to Document

10.1109/BIBM49941.2020.9313582

Cite this

Park, S., Khan, S., Moinuddin, M., & Al-Saggaf, U. M. (2020). GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 1096-1099). Article 9313582 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313582

Park, Seongyong ; Khan, Shujaat ; Moinuddin, Muhammad et al. / GSSMD : A new standardized effect size measure to improve robustness and interpretability in biological applications. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1096-1099 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{110b90fba4ca4aa3b0a5041659811396,

title = "GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications",

abstract = "In many biological applications, the primary objective of study is to quantity 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.",

keywords = "Biological applications, Interpretability, Overlap statistics, Robustness, Standardized effect size measure",

author = "Seongyong Park and Shujaat Khan and Muhammad Moinuddin and Al-Saggaf, \{Ubaid M.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313582",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1096--1099",

editor = "Taesung Park and Young-Rae Cho and Hu, \{Xiaohua Tony\} and Illhoi Yoo and Woo, \{Hyun Goo\} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

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}

Park, S, Khan, S, Moinuddin, M & Al-Saggaf, UM 2020, GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313582, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1096-1099. https://doi.org/10.1109/BIBM49941.2020.9313582

GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications. / Park, Seongyong; Khan, Shujaat; Moinuddin, Muhammad et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1096-1099 9313582 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Park, Seongyong

AU - Khan, Shujaat

AU - Moinuddin, Muhammad

AU - Al-Saggaf, Ubaid M.

PY - 2020/12/16

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N2 - In many biological applications, the primary objective of study is to quantity 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.

AB - In many biological applications, the primary objective of study is to quantity 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.

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M3 - Conference contribution

AN - SCOPUS:85100335697

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

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BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

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A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

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A2 - Kang, Mingon

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Park S, Khan S, Moinuddin M, Al-Saggaf UM. GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1096-1099. 9313582. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313582

GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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