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

Journal of the American Statistical Association
Sally PicciottoJ 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 ill...Continue Reading

References

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Citations

Mar 15, 2018·American Journal of Epidemiology·Oliver Dukes, Stijn Vansteelandt
Dec 22, 2017·Basic & Clinical Pharmacology & Toxicology·Anton PottegårdShahram Bahmanyar
Dec 5, 2012·Statistics in Medicine·R M DanielJ A C Sterne
Aug 23, 2018·Biometrics·Oliver DukesStijn Vansteelandt
Feb 23, 2019·Journal of Biopharmaceutical Statistics·Aniek SiesIven Van Mechelen
Apr 25, 2015·Journal of Causal Inference·Maya PetersenMark van der Laan
Oct 1, 2019·Journal of the American Statistical Association·Jessica G YoungMiguel A Hernán
Mar 13, 2019·Journal of Biopharmaceutical Statistics·Yang LiuMing-Hui Chen
May 14, 2021·Biometrical Journal. Biometrische Zeitschrift·Ruth H KeoghStijn Vansteelandt

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Software Mentioned

SNCFTM

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