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Direct estimation of the mean outcome on treatment when treatment assignment and discontinuation compete

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  • Xin Lu
  • Brent A. Johnson

Abstract

Several authors have investigated the challenges of statistical analyses and inference in the presence of early treatment termination, including a loss of efficiency in randomized controlled trials and a connection to dynamic regimes in observational studies. Popular estimation strategies for causal estimands in dynamic regimes lend themselves to studies where treatment is assigned at a finite number of points and the extension to continuous treatment assignment is nontrivial. We re-examine this from a different perspective and propose a new estimator for the mean outcome of a target treatment length policy that does not involve a treatment model. Because this strategy avoids modelling the treatment assignment mechanism, the estimator works for both discrete and continuous treatment length data and eschews bias and imprecision that arise as a result of coarsening continuous time data into intervals. We show how the competition of treatment length assignment and terminating event lead to a competing risks problem. We exemplify the direct estimator through numerical studies and the analysis of two real datasets. When all modelling assumptions for both the direct and inverse weighted estimators are correct, our simulation studies suggest that the direct estimator is more precise.

Suggested Citation

  • Xin Lu & Brent A. Johnson, 2015. "Direct estimation of the mean outcome on treatment when treatment assignment and discontinuation compete," Biometrika, Biometrika Trust, vol. 102(4), pages 797-807.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:797-807.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv043
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    References listed on IDEAS

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    1. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    2. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
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    Cited by:

    1. Xin Lu & Brent A. Johnson, 2017. "Direct estimation for adaptive treatment length policies: Methods and application to evaluating the effect of delayed PEG insertion," Biometrics, The International Biometric Society, vol. 73(3), pages 981-989, September.
    2. Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A., 2020. "Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 300-314, January.
    3. Hao Sun & Ashkan Ertefaie & Brent A. Johnson, 2022. "Estimating mean potential outcome under adaptive treatment length strategies in continuous time," Biometrics, The International Biometric Society, vol. 78(4), pages 1503-1514, December.

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