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Articles 1 - 5 of 5
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.
Semiparametric Causal Inference In Matched Cohort Studies, Edward Kennedy, Arvid Sjolander, Dylan Small
Semiparametric Causal Inference In Matched Cohort Studies, Edward Kennedy, Arvid Sjolander, Dylan Small
Edward H. Kennedy
Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimators, which ignore the matching and exposure-based sampling. This is not the case for general average effects. We also show that matched cohort studies are often more efficient …
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 …
Optimal Restricted Estimation For More Efficient Longitudinal Causal Inference, Edward Kennedy, Marshall Joffe, Dylan Small
Optimal Restricted Estimation For More Efficient Longitudinal Causal Inference, Edward Kennedy, Marshall Joffe, Dylan Small
Edward H. Kennedy
Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions.
Instrumental Variable Analyses: Exploiting Natural Randomness To Understand Causal Mechanisms, Theodore Iwashyna, Edward Kennedy
Instrumental Variable Analyses: Exploiting Natural Randomness To Understand Causal Mechanisms, Theodore Iwashyna, Edward Kennedy
Edward H. Kennedy
Instrumental variable analysis is a technique commonly used in the social sciences to provide evidence that a treatment causes an outcome, as contrasted with evidence that a treatment is merely associated with differences in an outcome. To extract such strong evidence from observational data, instrumental variable analysis exploits situations where some degree of randomness affects how patients are selected for a treatment. An instrumental variable is a characteristic of the world that leads some people to be more likely to get the specific treatment we want to study but does not otherwise change thosepatients’ outcomes. This seminar explains, in nonmathematical …