IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v214y2026ics0167947325001537.html

Gamma approximation of stratified truncated exact test (GASTE-test) & application

Author

Listed:
  • Wendling, Alexandre
  • Galiez, Clovis

Abstract

The analysis of binary outcomes and features, such as the effect of vaccination on health, often rely on 2 × 2 contingency tables. However, confounding factors such as age or gender call for stratified analysis, by creating sub-tables, which is common in bioscience, epidemiological, and social research, as well as in meta-analyses. Traditional methods for testing associations across strata, such as the Cochran-Mantel-Haenszel (CMH) test, struggle with small sample sizes and heterogeneity of effects between strata. Exact tests can address these issues, but are computationally expensive. To address these challenges, the Gamma Approximation of Stratified Truncated Exact (GASTE) test is proposed. It approximates the exact statistic of the combination of p-values with discrete support, leveraging the gamma distribution to approximate the distribution of the test statistic under stratification, providing fast and accurate p-value calculations, even when effects vary between strata. The GASTE method maintains high statistical power and low type I error rates, outperforming traditional methods by offering more sensitive and reliable detection. It is computationally efficient and broadens the applicability of exact tests in research fields with stratified binary data. The GASTE method is demonstrated through two applications: an ecological study of Alpine plant associations and a 1973 case study on admissions at the University of California, Berkeley. The GASTE method offers substantial improvements over traditional approaches. The GASTE method is available as an open-source package at https://github.com/AlexandreWen/gaste. A Python package is available on PyPI at https://pypi.org/project/gaste-test/

Suggested Citation

  • Wendling, Alexandre & Galiez, Clovis, 2026. "Gamma approximation of stratified truncated exact test (GASTE-test) & application," Computational Statistics & Data Analysis, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:csdana:v:214:y:2026:i:c:s0167947325001537
    DOI: 10.1016/j.csda.2025.108277
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947325001537
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2025.108277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kost, James T. & McDermott, Michael P., 2002. "Combining dependent P-values," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 183-190, November.
    2. Yaowu Liu & Jun Xie, 2020. "Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 393-402, January.
    3. Hong Zhang & Zheyang Wu, 2023. "The generalized Fisher's combination and accurate p‐value calculation under dependence," Biometrics, The International Biometric Society, vol. 79(2), pages 1159-1172, June.
    4. Eisend, Martin & Tarrahi, Farid, 2014. "Meta-analysis selection bias in marketing research," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 317-326.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meida Wang & Shuanglin Zhang & Qiuying Sha, 2022. "A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-13, April.
    2. Zichen Zhang & Ye Eun Bae & Jonathan R. Bradley & Lang Wu & Chong Wu, 2022. "SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Kwangmoon Park & Zhongxuan Sun & Ruiqi Liao & Emery H. Bresnick & Sündüz Keleş, 2026. "Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics," Nature Communications, Nature, vol. 17(1), pages 1-13, December.
    4. Yuzhuo Ma & Yanlong Zhao & Ji-Feng Zhang & Wenjian Bi, 2025. "Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    5. Daniele Massacci & Lucio Sarno & Lorenzo Trapani & Pierluigi Vallarino, 2025. "A General Randomized Test for Alpha," Tinbergen Institute Discussion Papers 25-045/III, Tinbergen Institute.
    6. Huda Alsulami & Silvia Liverani, 2025. "Evaluating the Cauchy combination test for count data," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-17, October.
    7. Vicente Gallego & Ramon Oller, 2024. "A Kernel approach for extending nonparametric multivariate analysis of variance in high-dimensional settings," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1315-1335, November.
    8. Wang, Guanpeng & Wu, Jiujing & Cui, Hengjian, 2024. "Cross projection test for mean vectors via multiple random splits in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    9. Lap Sum Chan & Gen Li & Eric B. Fauman & Xianyong Yin & Markku Laakso & Michael Boehnke & Peter X. K. Song, 2025. "DrFARM: identification of pleiotropic genetic variants in genome-wide association studies," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    10. Xia, Liqi & Cao, Ruiyuan & Du, Jiang & Liu, Ling, 2026. "Rank-based combination independence tests for high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 212(C).
    11. Arie Shaus & Yana Gerber & Shira Faigenbaum-Golovin & Barak Sober & Eli Piasetzky & Israel Finkelstein, 2020. "Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-15, September.
    12. Eisend, Martin & Hermann, Erik, 2020. "Sexual orientation and consumption: Why and when do homosexuals and heterosexuals consume differently?," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 678-696.
    13. Wesley James Johnston & Angelina Nhat Hanh Le & Julian Ming-Sung Cheng, 2018. "A meta-analytic review of influence strategies in marketing channel relationships," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 674-702, July.
    14. Seonghun Cho & Minsup Shin & Young Hyun Cho & Johan Lim, 2025. "Change point detection in high dimensional covariance matrix using Pillai’s statistics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(1), pages 53-84, March.
    15. Xiduo Chen & Xingdong Feng & Antonio F. Galvao & Yeheng Ge, 2025. "Treatment Effects Inference with High-Dimensional Instruments and Control Variables," Papers 2503.20149, arXiv.org, revised Oct 2025.
    16. Okazaki, Shintaro & Eisend, Martin & Plangger, Kirk & de Ruyter, Ko & Grewal, Dhruv, 2020. "Understanding the Strategic Consequences of Customer Privacy Concerns: A Meta-Analytic Review," Journal of Retailing, Elsevier, vol. 96(4), pages 458-473.
    17. Lulu Shang & Peijun Wu & Xiang Zhou, 2025. "Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    18. F. Fouladi & Y. Chen & S. Bera & A. K. Jarmusch & D. Van Tyne & F. J. Palella & J. B. Margolick & K. W. Chew & J. Sun & J. Martinson & C. R. Rinaldo & S. D. Peddada, 2025. "A taxon-specific measurement of disruption in a multi-modal study of microbiomes and metabolomes reveals system-wide dysbiosis preceding HIV-1 infection," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    19. Nikolaus Seitz & Martina Buratti & Erik E. Lehmann & Julie Kurrle, 2026. "A meta-analysis towards the effectiveness of startup accelerators," The Journal of Technology Transfer, Springer, vol. 51(1), pages 373-413, February.
    20. Congran Yu & Hengjian Cui, 2025. "Feature screening via false discovery rate control for linear model with multivariate responses," Statistical Papers, Springer, vol. 66(2), pages 1-29, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:214:y:2026:i:c:s0167947325001537. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.