Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Biostatistics
Nonparametric Methods For Doubly Robust Estimation Of Continuous Treatment Effects, Edward Kennedy, Zongming Ma, Matthew Mchugh, Dylan Small
Nonparametric Methods For Doubly Robust Estimation Of Continuous Treatment Effects, Edward Kennedy, Zongming Ma, Matthew Mchugh, Dylan Small
Edward H. Kennedy
Continuous treatments (e.g., doses) arise often in practice, but available causal effect estimators require either parametric models for the effect curve or else consistent estimation of a single nuisance function. We propose a novel doubly robust kernel smoothing approach, which requires only mild smoothness assumptions on the effect curve and allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and also discuss an approach for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.
Surrogate Markers For Time-Varying Treatments And Outcomes, Jesse Hsu, Edward Kennedy, Jason Roy, Alisa Stephens-Shields, Dylan Small, Marshall Joffe
Surrogate Markers For Time-Varying Treatments And Outcomes, Jesse Hsu, Edward Kennedy, Jason Roy, Alisa Stephens-Shields, Dylan Small, Marshall Joffe
Edward H. Kennedy
A surrogate marker is a variable commonly used in clinical trials to guide treatment decisions when the outcome of ultimate interest is not available. A good surrogate marker is one where the treatment effect on the surrogate is a strong predictor of the effect of treatment on the outcome. We review the situation when there is one treatment delivered at baseline, one surrogate measured at one later time point, and one ultimate outcome of interest and discuss new issues arising when variables are time-varying. Most of the literature on surrogate markers has only considered simple settings with one treatment, one …