Documents de recherche

@article{D2022,
title = {Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models},
author = {D, Rodriguez Duque; D, Stephens; EEM, Moodie; MB, Klein;},
url = {https://academic.oup.com/biostatistics/advance-article-abstract/doi/10.1093/biostatistics/kxac007/6564195?redirectedFrom=fulltext&login=false},
doi = {10.1093/biostatistics/kxac007},
year = {2022},
date = {2022-01-01},
journal = {Biostatistics},
abstract = {Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of uncertainty, including at the individual level. In this work, we extend the use of recently developed Bayesian methods for Marginal Structural Models to arrive at inference of DTRs. We do this (i) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then (ii) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from nonparametric prior assumptions on the observational world data-generating process. Our approach relies on Bayesian semiparametric inference, where inference about a finite-dimensional parameter is made all while working within an infinite-dimensional space of distributions. We further study Bayesian inference of DTRs in the double robust setting by using posterior predictive inference and the nonparametric Bayesian bootstrap. The proposed methods allow for uncertainty quantification at the individual level, thereby enabling personalized decision-making. We examine the performance of these methods via simulation and demonstrate their utility by exploring whether to adapt HIV therapy to a measure of patients' liver health, in order to minimize liver scarring.},
keywords = {Bayesian inference, Dynamic treatment regimes, Marginal structural models},
pubstate = {published},
tppubtype = {article}
}

@article{RP2019,
title = {Evaluating Flexible Modeling of Continuous Covariates in Inverse-Weighted Estimators},
author = {Kyle RP and Moodie EEM and Klein MB and Abrahamowicz M},
url = {https://pubmed.ncbi.nlm.nih.gov/30649165/},
doi = {10.1093/aje/kwz004},
year = {2019},
date = {2019-06-01},
journal = {American Journal of Epidemiology},
abstract = {Correct specification of the exposure model is essential for unbiased estimation in marginal structural models with inverse-probability-of-treatment weights. However, although flexible modeling is commonplace when estimating effects of continuous covariates in outcome models, its use is less frequent in estimation of inverse probability weights. Using simulations, we assess the accuracy of the treatment effect estimates and covariate balance obtained with different exposure model specifications when the true relationship between a continuous, possibly time-varying covariate Lt and the logit of the probability of exposure is nonlinear. Specifically, we compare 4 approaches to modeling the effect of Lt when estimating inverse probability weights: a linear function, the covariate-balancing propensity score, and 2 easy-to-implement flexible methods that relax the assumption of linearity: cubic regression splines and fractional polynomials. Using data from 2 empirical studies, we compare linear exposure models with flexible exposure models to estimate the effect of sustained virological response to hepatitis C virus treatment on the progression of liver fibrosis. Our simulation results demonstrate that ignoring important nonlinear relationships when fitting the exposure model may provide poorer covariate balance and induce substantial bias in the estimated exposure-outcome associations. Analysts should routinely consider flexible modeling of continuous covariates when estimating inverse-probability-of-treatment weights.},
keywords = {Causal inference, Fractional polynomials, Marginal structural models, Model misspecification, Splines},
pubstate = {published},
tppubtype = {article}
}

@article{W2018b,
title = {Association between depressive symptoms, CD4 count and HIV viral suppression among HIV-HCV co-infected people},
author = {Aibibula W and Cox J and Hamelin AM and Moodie EEM and Anema A and Klein MB and Brassard P},
url = {https://pubmed.ncbi.nlm.nih.gov/29374972/},
doi = {10.1080/09540121.2018.1431385},
year = {2018},
date = {2018-05-01},
journal = {AIDS Care},
abstract = {Depressive symptoms are associated with poor HIV viral control and immune recovery among people living with HIV. However, no prior studies assessed this association exclusively among people co-infected with HIV-hepatitis C virus (HCV). While people with HIV only and those with HIV-HCV co-infection share many characteristics, co-infected people may become more susceptible to the effects of depressive symptoms on health outcomes. We assessed this association exclusively among people co-infected with HIV-HCV in Canada using data from the Food Security & HIV-HCV Sub-Study (FS Sub-Study) of the Canadian Co-Infection Cohort (CCC). Stabilized inverse probability weighted marginal structural model was used to account for potential time-varying confounders. A total of 725 participants were enrolled between 2012 and 2015. At baseline, 52% of participants reported depressive symptoms, 75% had undetectable HIV viral load, and median CD4 count was 466 (IQR 300-665). People experiencing depressive symptoms had 1.32 times (95% CI: 1.07, 1.63) the risk of having detectable HIV viral load, but had comparable CD4 count to people who did not experience depressive symptoms (fold change of CD4 = 0.96, 95% CI: 0.91, 1.03). Presence of depressive symptoms is a risk factor for incomplete short-term HIV viral suppression among people co-infected with HIV-HCV. Therefore, depressive symptoms screening and related counseling may improve HIV related health outcomes and reduce HIV transmission.},
keywords = {CD4 count, Depressive symptoms, HIV viral load, HIV-HCV co-infection, Marginal structural models},
pubstate = {published},
tppubtype = {article}
}

@article{W2018,
title = {Food insecurity may lead to incomplete HIV viral suppression and less immune reconstitution among HIV/hepatitis C virus-coinfected people},
author = {Aibibula W and Cox J and Hamelin AM and Moodie EEM and Naimi AI and McLinden T and Klein MB and Brassard P},
url = {https://pubmed.ncbi.nlm.nih.gov/29094807/},
doi = {10.1111/hiv.12561},
year = {2018},
date = {2018-02-01},
journal = {HIV Medicine},
abstract = {Objectives: The aim of this study was to determine the impact of food insecurity (FI) on HIV viral load and CD4 count among people coinfected with HIV and hepatitis C virus (HCV).

Methods: This study was conducted using data from the Food Security & HIV-HCV Sub-Study of the Canadian Co-Infection Cohort study. FI was measured using the adult scale of Health Canada's Household Food Security Survey Module and was classified into three categories: food security, moderate food insecurity and severe food insecurity. The association between FI, HIV viral load, and CD4 count was assessed using a stabilized inverse probability weighted marginal structural model.

Results: A total of 725 HIV/HCV-coinfected people with 1973 person-visits over 3 years of follow-up contributed to this study. At baseline, 23% of participants experienced moderate food insecurity and 34% experienced severe food insecurity. The proportion of people with undetectable HIV viral load was 75% and the median CD4 count was 460 [interquartile range (IQR): 300-665] cells/μL. People experiencing severe food insecurity had 1.47 times [95% confidence interval (CI): 1.14, 1.88] the risk of having detectable HIV viral load and a 0.91-fold (95% CI: 0.84, 0.98) increase in CD4 count compared with people who were food secure.

Conclusions: These findings provide evidence of the negative impact of food insecurity on HIV viral load and CD4 count among HIV/HCV-coinfected people.},
keywords = {CD4 count, Food insecurity, HIV viral load, HIV-HCV co-infection, Marginal structural models},
pubstate = {published},
tppubtype = {article}
}

@article{W2017b,
title = {Impact of Food Insecurity on Depressive Symptoms Among HIV-HCV Co-infected People},
author = {Aibibula W and Cox J and Hamelin AM and Moodie EEM and Naimi AI and McLinden T and Klein MB and Brassard P},
doi = {10.1007/s10461-017-1942-z},
year = {2017},
date = {2017-12-01},
journal = {AIDS and Behaviour},
abstract = {Food insecurity (FI) is associated with depressive symptoms among HIV mono-infected people. Our objective was to examine to what extent this association holds among HIV-hepatitis C virus (HCV) co-infected people. We used data from a prospective cohort study of HIV-HCV co-infected people in Canada. FI was measured using the ten-item adult scale of Health Canada's Household Food Security Survey Module and was classified into three categories: food secure, moderate FI, and severe FI. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CES-D-10) and was classified into absence or presence of depressive symptoms. FI, depressive symptoms, and other covariates were updated every 6 months. The association between FI and depressive symptoms was assessed using a stabilized inverse probability weighted marginal structural model. The study sample included 725 HIV-HCV co-infected people with 1973 person-visits over 3 years of follow up. At baseline, 23% of participants experienced moderate food insecurity, 34% experienced severe food insecurity and 52% had depressive symptoms. People experiencing moderate FI had 1.63 times (95% CI 1.44-1.86) the risk of having depressive symptoms and people experiencing severe FI had 2.01 times (95% CI 1.79-2.25) the risk of having depressive symptoms compared to people who were food secure. FI is a risk factor for developing depressive symptoms among HIV-HCV co-infected people. Food supplementation, psychosocial support and counseling may improve patient health outcomes.},
keywords = {Depression, Food insecurity, HIV-HCV co-infection, Marginal structural models},
pubstate = {published},
tppubtype = {article}
}

@article{Kyle2016,
title = {Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models},
author = {Ryan P. Kyle and Erica E. M. Moodie and Marina B. Klein and Michał Abrahamowicz},
url = {https://www.ncbi.nlm.nih.gov/pubmed/27416840},
doi = {10.1093/aje/kww068},
year = {2016},
date = {2016-08-15},
journal = {American Journal of Epidemiology },
abstract = {Unbiased estimation of causal parameters from marginal structural models (MSMs) requires a fundamental assumption of no unmeasured confounding. Unfortunately, the time-varying covariates used to obtain inverse probability weights are often error-prone. Although substantial measurement error in important confounders is known to undermine control of confounders in conventional unweighted regression models, this issue has received comparatively limited attention in the MSM literature. Here we propose a novel application of the simulation-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we compare 2 approaches. The direct approach to SIMEX-based correction targets outcome model parameters, while the indirect approach corrects the weights estimated using the exposure model. We assess the performance of the proposed methods in simulations under different clinically plausible assumptions. The simulations demonstrate that measurement errors in time-dependent covariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures, and that both proposed SIMEX approaches yield practically unbiased estimates in scenarios featuring low-to-moderate degrees of error. We illustrate the proposed approach in a simple analysis of the relationship between sustained virological response and liver fibrosis progression among persons infected with hepatitis C virus, while accounting for measurement error in γ-glutamyltransferase, using data collected in the Canadian Co-infection Cohort Study from 2003 to 2014.
© The Author 2016. 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 = {Causal inference, Marginal structural models, Measurement error, Simulations, Time-varying covariates},
pubstate = {published},
tppubtype = {article}
}

@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{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}
}

@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}
}