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Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions

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  • Sally Picciotto
  • Miguel A. Hernán
  • John H. Page
  • Jessica G. Young
  • James M. Robins

Abstract

In the presence of time-varying confounders affected by prior treatment, standard statistical methods for failure time analysis may be biased. Methods that correctly adjust for this type of covariate include the parametric g-formula, inverse probability weighted estimation of marginal structural Cox proportional hazards models, and g-estimation of structural nested accelerated failure time models. In this article, we propose a novel method to estimate the causal effect of a time-dependent treatment on failure in the presence of informative right-censoring and time-dependent confounders that may be affected by past treatment: g-estimation of structural nested cumulative failure time models (SNCFTMs). An SNCFTM considers the conditional effect of a final treatment at time m on the outcome at each later time k by modeling the ratio of two counterfactual cumulative risks at time k under treatment regimes that differ only at time m . Inverse probability weights are used to adjust for informative censoring. We also present a procedure that, under certain “no-interaction” conditions, uses the g-estimates of the model parameters to calculate unconditional cumulative risks under nondynamic (static) treatment regimes. The procedure is illustrated with an example using data from a longitudinal cohort study, in which the “treatments” are healthy behaviors and the outcome is coronary heart disease.

Suggested Citation

  • Sally Picciotto & Miguel A. Hernán & John H. Page & Jessica G. Young & James M. Robins, 2012. "Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 886-900, September.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:886-900 DOI: 10.1080/01621459.2012.682532
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    References listed on IDEAS

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    1. Perron, Pierre & Yabu, Tomoyoshi, 2009. "Estimating deterministic trends with an integrated or stationary noise component," Journal of Econometrics, Elsevier, vol. 151(1), pages 56-69, July.
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    Cited by:

    1. Petersen Maya & Schwab Joshua & van der Laan Mark & Gruber Susan & Blaser Nello & Schomaker Michael, 2014. "Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 1-39, September.

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