98 citations · 166 across the 13 of their papers we have counts for
13 papers
Grace periods in comparative effectiveness studies of sustained treatments
Kerollos Nashat Wanis, Aaron L. Sarvet, Lan Wen +4
Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging f…
Causal inference with recurrent and competing events
Matias Janvin, Jessica G. Young, Pål C. Ryalen +1
Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effe…
Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis
Lan Wen, Julia Marcus, Jessica Young
The generalized g-formula can be used to estimate the probability of survival under a sustained treatment strategy. When treatment strategies are deterministic, estimators derived…
Revisiting the g-null paradox
Sean McGrath, Jessica G. Young, Miguel A. Hernán
The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formul…
Conditional separable effects
Mats J. Stensrud, James M. Robins, Aaron Sarvet +2
Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of diffe…
Generalized interpretation and identification of separable effects in competing event settings
Mats J. Stensrud, Miguel A. Hernán, Eric J. Tchetgen Tchetgen +3
In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However,…
Causal inference with limited resources: proportionally-representative interventions
Aaron L. Sarvet, Kerollos N. Wanis, Jessica Young +3
Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment…
gfoRmula: An R package for estimating effects of general time-varying treatment interventions via the parametric g-formula
Victoria Lin, Sean McGrath, Zilu Zhang +4
Researchers are often interested in using longitudinal data to estimate the causal effects of hypothetical time-varying treatment interventions on the mean or risk of a future outc…
More testing or more disease? A counterfactual approach to explaining observed increases in positive tests over time
Jessica G. Young, Sarah J. Willis, Katherine Hsu +2
Observed gonorrhea case rates (number of positive tests per 100,000 individuals) increased by 75 percent in the United States between 2009 and 2017, predominantly among men. Howeve…
Separable Effects for Causal Inference in the Presence of Competing Events
Mats J. Stensrud, Jessica G. Young, Vanessa Didelez +2
In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a…
A distributed regression analysis application based on SAS software Part II: Cox proportional hazards regression
Yury Vilk, Zilu Zhang, Jessica Young +4
Previous work has demonstrated the feasibility and value of conducting distributed regression analysis (DRA), a privacy-protecting analytic method that performs multivariable-adjus…
A distributed regression analysis application based on SAS software. Part I: Linear and logistic regression
Qoua L. Her, Yury Vilk, Jessica Young +4
Previous work has demonstrated the feasibility and value of conducting distributed regression analysis (DRA), a privacy-protecting analytic method that performs multivariable-adjus…
A causal framework for classical statistical estimands in failure time settings with competing events
Jessica G. Young, Mats J. Stensrud, Eric J. Tchetgen Tchetgen +1
In failure-time settings, a competing risk event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing…