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Full-Text Articles in Statistical Methodology
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 …
Semiparametric Theory And Empirical Processes In Causal Inference, Edward Kennedy
Semiparametric Theory And Empirical Processes In Causal Inference, Edward Kennedy
Edward H. Kennedy
In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory …
Comparison Of Methods For Estimating The Effect Of Salvage Therapy In Prostate Cancer When Treatment Is Given By Indication., Jeremy Taylor, Jincheng Shen, Edward Kennedy, Lu Wang, Douglas Schaubel
Comparison Of Methods For Estimating The Effect Of Salvage Therapy In Prostate Cancer When Treatment Is Given By Indication., Jeremy Taylor, Jincheng Shen, Edward Kennedy, Lu Wang, Douglas Schaubel
Edward H. Kennedy
For patients who were previously treated for prostate cancer, salvage hormone therapy is frequently given when the longitudinal marker prostate-specific antigen begins to rise during follow-up. Because the treatment is given by indication, estimating the effect of the hormone therapy is challenging. In a previous paper we described two methods for estimating the treatment effect, called two-stage and sequential stratification. The two-stage method involved modeling the longitudinal and survival data. The sequential stratification method involves contrasts within matched sets of people, where each matched set includes people who did and did not receive hormone therapy. In this paper, we evaluate …