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A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes

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  • Patrick M. Schnell
  • Richard Baumgartner
  • Shahrul Mt‐Isa
  • Vladimir Svetnik

Abstract

Chronic diseases often require continuing care, and early response to treatment can be an important predictor of long‐term efficacy. Often, an apparent lack of early efficacy may lead to discontinuation of treatment, with the decision made either by clinicians or by the patients themselves. Thus, it is important to determine whether or not a desired early outcome corresponds to a beneficial long‐term effect of continuing treatment, and conversely, whether or not the absence of such an outcome corresponds to a lack of long‐term benefit. However, primary clinical trials of such treatments are not commonly designed to answer such questions, for example by randomizing subjects to continue or discontinue treatment after observing early outcomes. We propose an approach to estimating the effect of continuing treatment after observing early outcomes using data from randomized controlled trials in which treatment discontinuation was not part of the design. Our approach estimates average causal effects of continuing treatment on long‐term outcomes in principal strata defined by the potential early outcomes under treatment. For illustration, we estimate the effects of continuing to take gaboxadol to treat insomnia conditional on early improvement in subjective sleep quality after two nights, based on a standard parallel‐arm randomized controlled trial.

Suggested Citation

  • Patrick M. Schnell & Richard Baumgartner & Shahrul Mt‐Isa & Vladimir Svetnik, 2022. "A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1065-1084, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1065-1084
    DOI: 10.1111/rssc.12552
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    References listed on IDEAS

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    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    4. Gustafson Paul, 2010. "Bayesian Inference for Partially Identified Models," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-20, March.
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