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Assessing replicability of findings across two studies of multiple features

Author

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  • Marina Bogomolov
  • Ruth Heller
Abstract
SummaryReplicability analysis aims to identify the overlapping signals across independent studies that examine the same features. For this purpose we develop hypothesis testing procedures that first select the promising features from each of two studies separately. Only those features selected in both studies are then tested. The proposed procedures have theoretical guarantees regarding their control of the familywise error rate or false discovery rate on the replicability claims. They can also be used for signal discovery in each study separately, with the desired error control. Their power for detecting truly replicable findings is compared to alternatives. We illustrate the procedures on behavioural genetics data.

Suggested Citation

  • Marina Bogomolov & Ruth Heller, 2018. "Assessing replicability of findings across two studies of multiple features," Biometrika, Biometrika Trust, vol. 105(3), pages 505-516.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:3:p:505-516.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy029
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    References listed on IDEAS

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    6. Helmut Finner & Veronika Gontscharuk, 2009. "Controlling the familywise error rate with plug‐in estimator for the proportion of true null hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 1031-1048, November.
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    Cited by:

    1. Ran Dai & Cheng Zheng, 2023. "False discovery rate‐controlled multiple testing for union null hypotheses: a knockoff‐based approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3497-3509, December.

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