Research Papers

@article{Mojaverian2015,
title = {The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data},
author = {Nassim Mojaverian and Erica E. M. Moodie and Alex Bliu and Marina B. Klein},
url = {https://www.ncbi.nlm.nih.gov/pubmed/26589708},
doi = {10.1093/aje/kwv152},
year = {2015},
date = {2015-12-15},
journal = {American Journal of Epidemiology},
abstract = {The impact of risk factors on the amount of time taken to reach an endpoint is a common parameter of interest. Hazard ratios are often estimated using a discrete-time approximation, which works well when the by-interval event rate is low. However, if the intervals are made more frequent than the observation times, missing values will arise. We investigated common analytical approaches, including available-case (AC) analysis, last observation carried forward (LOCF), and multiple imputation (MI), in a setting where time-dependent covariates also act as mediators. We generated complete data to obtain monthly information for all individuals, and from the complete data, we selected "observed" data by assuming that follow-up visits occurred every 6 months. MI proved superior to LOCF and AC analyses when only data on confounding variables were missing; AC analysis also performed well when data for additional variables were missing completely at random. We applied the 3 approaches to data from the Canadian HIV-Hepatitis C Co-infection Cohort Study (2003-2014) to estimate the association of alcohol abuse with liver fibrosis. The AC and LOCF estimates were larger but less precise than those obtained from the analysis that employed MI.
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.},
keywords = {Available-case analysis, Last observation carried forward, Marginal structural models, Missing data, Multiple imputation, Survival analysis},
pubstate = {published},
tppubtype = {article}
}

@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}
}

@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}
}