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Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models

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Abstract
Accounting for time-varying confounding when assessing the causal effects of time-varying exposures on survival time is challenging. Standard survival methods that incorporate time-varying confounders as covariates generally yield biased effect estimates. Estimators using weighting by inverse probability of exposure can be unstable when confounders are highly predictive of exposure or the exposure is continuous. Structural nested accelerated failure time models (AFTMs) require artificial recensoring, which can cause estimation difficulties. Here, we introduce the structural nested cumulative survival time model (SNCSTM). This model assumes that intervening to set exposure at time t to zero has an additive effect on the subsequent conditional hazard given exposure and confounder histories when all subsequent exposures have already been set to zero. We show how to fit it using standard software for generalized linear models and describe two more efficient, double robust, closed-form estimators. All three estimators avoid the artificial recensoring of AFTMs and the instability of estimators that use weighting by the inverse probability of exposure. We examine the performance of our estimators using a simulation study and illustrate their use on data from the UK Cystic Fibrosis Registry. The SNCSTM is compared with a recently proposed structural nested cumulative failure time model, and several advantages of the former are identified.
Keywords
General Biochemistry, Genetics and Molecular Biology, Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Agricultural and Biological Sciences, General Medicine, accelerated failure time model, Aalen's additive model, G-estimation, marginal structural model, survival data, CAUSAL INFERENCE

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MLA
Seaman, Shaun, et al. “Adjusting for Time‐varying Confounders in Survival Analysis Using Structural Nested Cumulative Survival Time Models.” BIOMETRICS, 2020.
APA
Seaman, S., Dukes, O., Keogh, R., & Vansteelandt, S. (2020). Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models. BIOMETRICS.
Chicago author-date
Seaman, Shaun, Oliver Dukes, Ruth Keogh, and Stijn Vansteelandt. 2020. “Adjusting for Time‐varying Confounders in Survival Analysis Using Structural Nested Cumulative Survival Time Models.” BIOMETRICS.
Chicago author-date (all authors)
Seaman, Shaun, Oliver Dukes, Ruth Keogh, and Stijn Vansteelandt. 2020. “Adjusting for Time‐varying Confounders in Survival Analysis Using Structural Nested Cumulative Survival Time Models.” BIOMETRICS.
Vancouver
1.
Seaman S, Dukes O, Keogh R, Vansteelandt S. Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models. BIOMETRICS. 2020;
IEEE
[1]
S. Seaman, O. Dukes, R. Keogh, and S. Vansteelandt, “Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models,” BIOMETRICS, 2020.
@article{8641261,
  abstract     = {Accounting for time-varying confounding when assessing the causal effects of time-varying exposures on survival time is challenging. Standard survival methods that incorporate time-varying confounders as covariates generally yield biased effect estimates. Estimators using weighting by inverse probability of exposure can be unstable when confounders are highly predictive of exposure or the exposure is continuous. Structural nested accelerated failure time models (AFTMs) require artificial recensoring, which can cause estimation difficulties. Here, we introduce the structural nested cumulative survival time model (SNCSTM). This model assumes that intervening to set exposure at time t to zero has an additive effect on the subsequent conditional hazard given exposure and confounder histories when all subsequent exposures have already been set to zero. We show how to fit it using standard software for generalized linear models and describe two more efficient, double robust, closed-form estimators. All three estimators avoid the artificial recensoring of AFTMs and the instability of estimators that use weighting by the inverse probability of exposure. We examine the performance of our estimators using a simulation study and illustrate their use on data from the UK Cystic Fibrosis Registry. The SNCSTM is compared with a recently proposed structural nested cumulative failure time model, and several advantages of the former are identified.},
  author       = {Seaman, Shaun and Dukes, Oliver and Keogh, Ruth and Vansteelandt, Stijn},
  issn         = {0006-341X},
  journal      = {BIOMETRICS},
  keywords     = {General Biochemistry,Genetics and Molecular Biology,Statistics and Probability,General Immunology and Microbiology,Applied Mathematics,General Agricultural and Biological Sciences,General Medicine,accelerated failure time model,Aalen's additive model,G-estimation,marginal structural model,survival data,CAUSAL INFERENCE},
  language     = {eng},
  pages        = {12},
  title        = {Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models},
  url          = {http://dx.doi.org/10.1111/biom.13158},
  year         = {2020},
}

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