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Change-Plane Analysis for Subgroup Detection and Sample Size Calculation

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  • Ailin Fan
  • Rui Song
  • Wenbin Lu

Abstract

We propose a systematic method for testing and identifying a subgroup with an enhanced treatment effect. We adopts a change-plane technique to first test the existence of a subgroup, and then identify the subgroup if the null hypothesis on nonexistence of such a subgroup is rejected. A semiparametric model is considered for the response with an unspecified baseline function and an interaction between a subgroup indicator and treatment. A doubly robust test statistic is constructed based on this model, and asymptotic distributions of the test statistic under both null and local alternative hypotheses are derived. Moreover, a sample size calculation method for subgroup detection is developed based on the proposed statistic. The finite sample performance of the proposed test is evaluated via simulations. Finally, the proposed methods for subgroup identification and sample size calculation are applied to a data from an AIDS study.

Suggested Citation

  • Ailin Fan & Rui Song & Wenbin Lu, 2017. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 769-778, April.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:518:p:769-778
    DOI: 10.1080/01621459.2016.1166115
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    References listed on IDEAS

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    1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
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    5. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    6. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    7. Xiao Song & Margaret Sullivan Pepe, 2004. "Evaluating Markers for Selecting a Patient's Treatment," Biometrics, The International Biometric Society, vol. 60(4), pages 874-883, December.
    8. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
    9. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
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    Cited by:

    1. Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    2. Peng Jin & Wenbin Lu & Yu Chen & Mengling Liu, 2023. "Change‐plane analysis for subgroup detection with a continuous treatment," Biometrics, The International Biometric Society, vol. 79(3), pages 1920-1933, September.
    3. Dana Johnson & Wenbin Lu & Marie Davidian, 2023. "A general framework for subgroup detection via one‐step value difference estimation," Biometrics, The International Biometric Society, vol. 79(3), pages 2116-2126, September.
    4. Ying Huang & Juhee Cho & Youyi Fong, 2021. "Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 291-311, March.
    5. Xu Gao & Weining Shen & Jing Ning & Ziding Feng & Jianhua Hu, 2022. "Addressing patient heterogeneity in disease predictive model development," Biometrics, The International Biometric Society, vol. 78(3), pages 1045-1055, September.

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