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Penalized Spline of Propensity Methods for Treatment Comparison

Author

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  • Tingting Zhou
  • Michael R. Elliott
  • Roderick J. A. Little

Abstract

Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a robust multiple imputation-based approach to causal inference in this setting called penalized spline of propensity methods for treatment comparison (PENCOMP), which builds on the penalized spline of propensity prediction method for missing data problems. PENCOMP estimates causal effects by imputing missing potential outcomes with flexible spline models and draws inference based on imputed and observed outcomes. Under the SUTVA, positivity, and ignorability assumptions, PENCOMP has a double robustness property for causal effects. Simulations suggest that it tends to outperform doubly robust marginal structural modeling when the weights are variable. We apply our method to the multicenter AIDS cohort study to estimate the effect of antiretroviral treatment on CD4 counts in HIV-infected patients. Supplementary materials for this article are available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.

Suggested Citation

  • Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2019. "Penalized Spline of Propensity Methods for Treatment Comparison," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 1-19, January.
  • Handle: RePEc:taf:jnlasa:v:114:y:2019:i:525:p:1-19
    DOI: 10.1080/01621459.2018.1518234
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    Citations

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    Cited by:

    1. Ray Chambers & Setareh Ranjbar & Nicola Salvati & Barbara Pacini, 2022. "Weighting, informativeness and causal inference, with an application to rainfall enhancement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1584-1612, October.
    2. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
    3. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    4. Antonio R. Linero, 2023. "Prior and posterior checking of implicit causal assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 3153-3164, December.
    5. Brian J. Reich & Shu Yang & Yawen Guan, 2022. "Discussion on “Spatial+: A novel approach to spatial confounding” by Dupont, Wood, and Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1291-1294, December.
    6. Ao Yuan & Anqi Yin & Ming T. Tan, 2021. "Enhanced Doubly Robust Procedure for Causal Inference," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 454-478, December.
    7. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2022. "Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies," Stats, MDPI, vol. 5(4), pages 1-17, December.
    8. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.

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