@manuascript {632984,
	title = {Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks},
	year = {In Preparation},
	abstract = {In semicompeting risks problems, nonterminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death. These settings are often modeled with illness-death models for the hazards of the terminal and nonterminal events, but evaluating causal treatment effects with hazard models is problematic due to conditioning on survival (a post-treatment outcome) that is embedded in the definition of a hazard. Extending an existing survivor average causal effect (SACE) estimand, we frame the evaluation of treatment effects in the context of semicompeting risks with principal stratification and introduce two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE). These principal causal effects are defined among units that would survive regardless of assigned treatment. We adopt a Bayesian estimation procedure that parameterizes illness-death models for both treatment arms. We outline a frailty specification that can accommodate within-person correlation between nonterminal and terminal event times, and we discuss potential avenues for adding model flexibility. The method is demonstrated in the context of hospital readmission among late-stage pancreatic cancer patients.},
	url = {https://arxiv.org/abs/1902.09304},
	author = {Leah Comment and Fabrizia Mealli and Sebastien Haneuse and Corwin Zigler}
}
@manuascript {632985,
	title = {Bayesian data fusion for unmeasured confounding},
	year = {Submitted},
	abstract = {Bayesian causal inference offers a principled approach to policy evaluation of proposed interventions on mediators or time-varying exposures. We outline a general approach to the estimation of causal quantities for settings with time-varying confounding, such as exposure-induced mediator-outcome confounders. We further extend this approach to propose two Bayesian data fusion (BDF) methods for unmeasured confounding. Using informative priors on quantities relating to the confounding bias parameters, our methods incorporate data from an external source where the confounder is measured in order to make inferences about causal estimands in the main study population. We present results from a simulation study comparing our data fusion methods to two common frequentist correction methods for unmeasured confounding bias in the mediation setting. We also demonstrate our method with an investigation of the role of stage at cancer diagnosis in contributing to Black-White colorectal cancer survival disparities.},
	url = {https://arxiv.org/abs/1902.10613},
	author = {Leah Comment and Brent A. Coull and Corwin Zigler and Valeri, Linda}
}
@article {jassal2016functional,
	title = {Functional dependence and mortality in the international dialysis outcomes and practice patterns study (DOPPS)},
	journal = {American Journal of Kidney Diseases},
	volume = {67},
	number = {2},
	year = {2016},
	pages = {283{\textendash}292},
	publisher = {Elsevier},
	author = {Jassal, S Vanita and Karaboyas, Angelo and Comment, Leah A and Bieber, Brian A and Morgenstern, Hal and Sen, Ananda and Gillespie, Brenda W and De Sequera, Patricia and Marshall, Mark R and Fukuhara, Shunichi and others}
}
