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Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models

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  • Judith Lok
  • Richard Gill
  • Aad Van Der Vaart
  • James Robins

Abstract

In this paper we review an approach to estimating the causal effect of a time‐varying treatment on time to some event of interest. This approach is designed for a situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time‐dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so‐called time‐dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time‐varying treatment, and present methods for estimating and testing the parameters of these models.

Suggested Citation

  • Judith Lok & Richard Gill & Aad Van Der Vaart & James Robins, 2004. "Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 271-295, August.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:3:p:271-295
    DOI: 10.1111/j.1467-9574.2004.00123.x
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    Cited by:

    1. Kevin He & Yun Li & Panduranga S. Rao & Randall S. Sung & Douglas E. Schaubel, 2020. "Prognostic score matching methods for estimating the average effect of a non-reversible binary time-dependent treatment on the survival function," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 451-470, July.
    2. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    3. Stephen Kastoryano & Bas van der Klaauw, 2022. "Dynamic evaluation of job search assistance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 227-241, March.
    4. Judith J. Lok & Victor DeGruttola, 2012. "Impact of Time to Start Treatment Following Infection with Application to Initiating HAART in HIV-Positive Patients," Biometrics, The International Biometric Society, vol. 68(3), pages 745-754, September.
    5. Yujie Xu & Vivian Loftness & Edson Severnini, 2021. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio," Energies, MDPI, vol. 14(14), pages 1-24, July.
    6. Rui Chen & Menggang Yu, 2021. "Tailored optimal posttreatment surveillance for cancer recurrence," Biometrics, The International Biometric Society, vol. 77(3), pages 942-955, September.
    7. Robin Henderson & Phil Ansell & Deyadeen Alshibani, 2010. "Regret-Regression for Optimal Dynamic Treatment Regimes," Biometrics, The International Biometric Society, vol. 66(4), pages 1192-1201, December.
    8. Daniel Commenges & Anne Gégout‐Petit, 2009. "A general dynamical statistical model with causal interpretation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 719-736, June.

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