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Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown

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  • Adam Ciarleglio
  • Eva Petkova
  • Todd Ogden
  • Thaddeus Tarpey

Abstract

Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To understand this heterogeneity better and to help in determining the best patient‐specific treatments for a given disease, many clinical trials are collecting large amounts of patient level data before administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover ‘biosignatures’ of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both identifies covariates associated with differential treatment effect and estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the approach proposed is justified via extensive simulation experiments and illustrated by using data from a placebo‐controlled clinical trial investigating antidepressant treatment response in subjects with depression.

Suggested Citation

  • Adam Ciarleglio & Eva Petkova & Todd Ogden & Thaddeus Tarpey, 2018. "Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1331-1356, November.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1331-1356
    DOI: 10.1111/rssc.12278
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    References listed on IDEAS

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    1. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
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    5. Adam Ciarleglio & Eva Petkova & R. Todd Ogden & Thaddeus Tarpey, 2015. "Treatment decisions based on scalar and functional baseline covariates," Biometrics, The International Biometric Society, vol. 71(4), pages 884-894, December.
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    8. Lu Tian & Ash A. Alizadeh & Andrew J. Gentles & Robert Tibshirani, 2014. "A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1517-1532, December.
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    Cited by:

    1. Wu Wang & Ying Sun & Huixia Judy Wang, 2023. "Latent group detection in functional partially linear regression models," Biometrics, The International Biometric Society, vol. 79(1), pages 280-291, March.
    2. Gao, Yuhe & Shi, Chengchun & Song, Rui, 2023. "Deep spectral Q-learning with application to mobile health," LSE Research Online Documents on Economics 119445, London School of Economics and Political Science, LSE Library.
    3. Zhang, Xiaoke & Xue, Wu & Wang, Qiyue, 2021. "Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    4. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    5. Hyung Park & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Functional additive models for optimizing individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(1), pages 113-126, March.

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