2015
Saarela, Olli; Stephens, David A.; Moodie, Erica E. M.; Klein, Marina B.
On Bayesian estimation of marginal structural models Journal Article
In: Biometrics, 2015.
Abstract | Links | BibTeX | Tags: Bayesian inference, Causal inference, Inverse probability weighting, Longitudinal data, Marginal structural models, Posterior predictive inference, Variance estimation
@article{Saarela2015,
title = {On Bayesian estimation of marginal structural models},
author = {Olli Saarela and David A. Stephens and Erica E. M. Moodie and Marina B. Klein},
url = {https://www.ncbi.nlm.nih.gov/pubmed/25677103},
doi = {10.1111/biom.12269},
year = {2015},
date = {2015-06-15},
journal = {Biometrics},
abstract = {The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without imbalances in measured covariates, thus removing the effects of confounding and informative censoring when performing inference. In this article, we formalize the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and show that this leads to a natural Bayesian interpretation of IPT weighted estimation. Using this interpretation, we are able to propose the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting. Our approach suggests that the weights should be derived from the posterior predictive treatment assignment and censoring probabilities, answering the question of whether and how the uncertainty in the estimation of the weights should be incorporated in Bayesian inference of marginal treatment effects. The proposed approach is compared to existing methods in simulated data, and applied to an analysis of the Canadian Co-infection Cohort.
© 2015, The International Biometric Society.},
keywords = {Bayesian inference, Causal inference, Inverse probability weighting, Longitudinal data, Marginal structural models, Posterior predictive inference, Variance estimation},
pubstate = {published},
tppubtype = {article}
}
The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without imbalances in measured covariates, thus removing the effects of confounding and informative censoring when performing inference. In this article, we formalize the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and show that this leads to a natural Bayesian interpretation of IPT weighted estimation. Using this interpretation, we are able to propose the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting. Our approach suggests that the weights should be derived from the posterior predictive treatment assignment and censoring probabilities, answering the question of whether and how the uncertainty in the estimation of the weights should be incorporated in Bayesian inference of marginal treatment effects. The proposed approach is compared to existing methods in simulated data, and applied to an analysis of the Canadian Co-infection Cohort.
© 2015, The International Biometric Society.
© 2015, The International Biometric Society.
2014
Moodie, Erica E. M.; Stephens, David A.; Klein, Marina B.
In: Statistics in Medicine, vol. 33, no. 8, pp. 1409-1425, 2014.
Abstract | Links | BibTeX | Tags: Causal inference, Completing risks, Confounding, Failure-time data, Intermediate variables, Inverse-probability weighting, Longitudinal data, Marginal structural models, Multiple-outcome data, Simulation, Survival analysis, Time-dependent confounding
@article{Moodie2014,
title = {A marginal structural model for multiple-outcome survival data:assessing the impact of injection drug use on several causes of death in the Canadian Co-infection Cohort},
author = {Erica E. M. Moodie and David A. Stephens and Marina B. Klein},
url = {https://www.ncbi.nlm.nih.gov/pubmed/24272681},
doi = {10.1002/sim.6043},
year = {2014},
date = {2014-04-15},
journal = {Statistics in Medicine},
volume = {33},
number = {8},
pages = {1409-1425},
abstract = {It is often the case that interest lies in the effect of an exposure on each of several distinct event types. For example, we are motivated to investigate in the impact of recent injection drug use on deaths due to each of cancer, end-stage liver disease, and overdose in the Canadian Co-infection Cohort (CCC). We develop a marginal structural model that permits estimation of cause-specific hazards in situations where more than one cause of death is of interest. Marginal structural models allow for the causal effect of treatment on outcome to be estimated using inverse-probability weighting under the assumption of no unmeasured confounding; these models are particularly useful in the presence of time-varying confounding variables, which may also mediate the effect of exposures. An asymptotic variance estimator is derived, and a cumulative incidence function estimator is given. We compare the performance of the proposed marginal structural model for multiple-outcome data to that of conventional competing risks models in simulated data and demonstrate the use of the proposed approach in the CCC.},
keywords = {Causal inference, Completing risks, Confounding, Failure-time data, Intermediate variables, Inverse-probability weighting, Longitudinal data, Marginal structural models, Multiple-outcome data, Simulation, Survival analysis, Time-dependent confounding},
pubstate = {published},
tppubtype = {article}
}
It is often the case that interest lies in the effect of an exposure on each of several distinct event types. For example, we are motivated to investigate in the impact of recent injection drug use on deaths due to each of cancer, end-stage liver disease, and overdose in the Canadian Co-infection Cohort (CCC). We develop a marginal structural model that permits estimation of cause-specific hazards in situations where more than one cause of death is of interest. Marginal structural models allow for the causal effect of treatment on outcome to be estimated using inverse-probability weighting under the assumption of no unmeasured confounding; these models are particularly useful in the presence of time-varying confounding variables, which may also mediate the effect of exposures. An asymptotic variance estimator is derived, and a cumulative incidence function estimator is given. We compare the performance of the proposed marginal structural model for multiple-outcome data to that of conventional competing risks models in simulated data and demonstrate the use of the proposed approach in the CCC.
Schnitzer, Mireille E.; Moodie, Erica E. M.; van der Laan, Mark J.; Platt, Robert W.; Klein, Marina B.
In: Biometrics, vol. 70, no. 1, pp. 144-152, 2014.
Abstract | Links | BibTeX | Tags: Double-robust, Inverse probability weighting, Kaplan-Meier, Longitudinal data, Marginal structural models, Survival analysis, Targeted maximum likelihood estimation
@article{Schnitzer2014,
title = {Modeling the impact of Hepatitis C viral clearance on End-stage liver disease in an HIV co-infected Cohort with targeted maximum likelihood estimation},
author = {Mireille E. Schnitzer and Erica E. M. Moodie and Mark J. van der Laan and Robert W. Platt and Marina B. Klein},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954273/},
doi = {10.1111/biom.12105},
year = {2014},
date = {2014-03-14},
journal = {Biometrics},
volume = {70},
number = {1},
pages = {144-152},
abstract = {Despite modern effective HIV treatment, Hepatitis C virus (HCV) Co-infection is associated with a high risk of progression to End-stage liver disease (ESLD) which has emerged as the primary cause of death in this population. Clinical interest lies in determining the impact of clearance of HCV on risk for ESLD. In this case study, we examine whether HCV clearance affects risk of ESLD using data from the multicenter Canadian Co-infection Cohort study. Complications in this survival analysis arise from the time-dependent nature of the data, the presence of baseline confounders, loss to follow-up, and confounders that change over time, all of which can obscure the causal effect of interest. Additional challenges included non-censoring variable missingness and event sparsity. In order to efficiently estimate the ESLD-free survival probabilities under a specific history of HCV clearance, we demonstrate the double-robust and semiparametric efficient method of Targeted Maximum Likelihood Estimation (TMLE). Marginal structural models (MSM) can be used to model the effect of viral clearance (expressed as a hazard ratio) on ESLD-free survival and we demonstrate a way to estimate the parameters of a logistic model for the hazard function with TMLE. We show the theoretical derivation of the efficient influence curves for the parameters of two different MSMs and how they can be used to produce variance approximations for parameter estimates. Finally, the data analysis evaluating the impact of HCV on ESLD was undertaken using multiple imputations to account for the non-monotone missing data.},
keywords = {Double-robust, Inverse probability weighting, Kaplan-Meier, Longitudinal data, Marginal structural models, Survival analysis, Targeted maximum likelihood estimation},
pubstate = {published},
tppubtype = {article}
}
Despite modern effective HIV treatment, Hepatitis C virus (HCV) Co-infection is associated with a high risk of progression to End-stage liver disease (ESLD) which has emerged as the primary cause of death in this population. Clinical interest lies in determining the impact of clearance of HCV on risk for ESLD. In this case study, we examine whether HCV clearance affects risk of ESLD using data from the multicenter Canadian Co-infection Cohort study. Complications in this survival analysis arise from the time-dependent nature of the data, the presence of baseline confounders, loss to follow-up, and confounders that change over time, all of which can obscure the causal effect of interest. Additional challenges included non-censoring variable missingness and event sparsity. In order to efficiently estimate the ESLD-free survival probabilities under a specific history of HCV clearance, we demonstrate the double-robust and semiparametric efficient method of Targeted Maximum Likelihood Estimation (TMLE). Marginal structural models (MSM) can be used to model the effect of viral clearance (expressed as a hazard ratio) on ESLD-free survival and we demonstrate a way to estimate the parameters of a logistic model for the hazard function with TMLE. We show the theoretical derivation of the efficient influence curves for the parameters of two different MSMs and how they can be used to produce variance approximations for parameter estimates. Finally, the data analysis evaluating the impact of HCV on ESLD was undertaken using multiple imputations to account for the non-monotone missing data.
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