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A modified false discovery rate multiple‐comparisons procedure for discrete data, applied to human immunodeficiency virus genetics

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  • Peter B. Gilbert

Abstract

Summary. To help to design vaccines for acquired immune deficiency syndrome that protect broadly against many genetic variants of the human immunodeficiency virus, the mutation rates at 118 positions in HIV amino‐acid sequences of subtype C versus those of subtype B were compared. The false discovery rate (FDR) multiple‐comparisons procedure can be used to determine statistical significance. When the test statistics have discrete distributions, the FDR procedure can be made more powerful by a simple modification. The paper develops a modified FDR procedure for discrete data and applies it to the human immunodeficiency virus data. The new procedure detects 15 positions with significantly different mutation rates compared with 11 that are detected by the original FDR method. Simulations delineate conditions under which the modified FDR procedure confers large gains in power over the original technique. In general FDR adjustment methods can be improved for discrete data by incorporating the modification proposed.

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  • Peter B. Gilbert, 2005. "A modified false discovery rate multiple‐comparisons procedure for discrete data, applied to human immunodeficiency virus genetics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 143-158, January.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:143-158
    DOI: 10.1111/j.1467-9876.2005.00475.x
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    Cited by:

    1. Wang, Li, 2022. "New testing procedures with k-FWER control for discrete data," Statistics & Probability Letters, Elsevier, vol. 180(C).
    2. Chen, Xiongzhi, 2019. "Uniformly consistently estimating the proportion of false null hypotheses via Lebesgue–Stieltjes integral equations," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 724-744.
    3. Döhler, Sebastian, 2018. "A discrete modification of the Benjamini–Yekutieli procedure," Econometrics and Statistics, Elsevier, vol. 5(C), pages 137-147.
    4. Marta Cousido‐Rocha & Jacobo de Uña‐Álvarez & Sebastian Döhler, 2022. "Multiple comparison procedures for discrete uniform and homogeneous tests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 219-243, January.
    5. Elsäßer Amelie & Victor Anja & Hommel Gerhard, 2011. "Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-21, November.
    6. Roelant Eijgelaar & Philip C De Witt Hamer & Carel F W Peeters & Frederik Barkhof & Marcel van Herk & Marnix G Witte, 2019. "Voxelwise statistical methods to localize practice variation in brain tumor surgery," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-12, September.

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