Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually and are often implausible, in part because adequate information on confounders is not available. In recent work, we have formalized variants of ignorability assumptions, which we term selective and future ignorability, which can more correctly represent the situation obtaining in many studies. Under selective ignorability, conditional independence obtains in a known subset of the data;under future ignorability, independence of treatment and potential outcomes holds conditionally on a combination of measured covariate history and future potential outcomes. We have developed initial approaches to inference which are more appropriate than standard methods when selective and/or future ignorability conditions obtain but standard ignorability conditions do not. In this project, we will extend our work on selective and future ignorability assumptions. To this end, we will 1) Extend our work in formulating these assumptions, in particular in considering estimating the effects of a treatment on a combination of repeated measures and failure-time outcomes;2) Investigate ways to make G-estimation a practical estimation option under these assumptions;3) Develop and investigate alternatives to G-estimation under selective and future ignorability;for this, we will consider maximum likelihood, targeted maximum likelihood, and Bayesian approaches;4) Use selective ignorability assumptions to develop more reliable methods of inference when the treatment of interest is mismeasured;and 5) Use the methods developed to estimate the effect of erythropoietin use on hematocrit levels and mortality among subjects with end-stage renal disease receiving chronic hemodialysis, using data from the United States Renal Data System. This is a topic that has recently generated substantial interest. Thus, the project will develop useful inferential methods for use under more reasonable assumptions, and will apply those methods to an important practical problem.
The project will develop and evaluate new assumptions for controlling confounding in longitudinal studies and new methods for estimating treatment effects under those assumptions. The project will use those methods in estimating the effect of erythropoietin on hematocrit and on mortality using data from the United States Renal Data System.
|Kennedy, Edward H; Joffe, Marshall M; Small, Dylan S (2015) Optimal restricted estimation for more efficient longitudinal causal inference. Stat Probab Lett 97:185-191|
|Hsu, Jesse Y; Kennedy, Edward H; Roy, Jason A et al. (2015) Surrogate markers for time-varying treatments and outcomes. Clin Trials 12:309-16|
|Yang, Wei; Joffe, Marshall M; Hennessy, Sean et al. (2014) Covariance adjustment on propensity parameters for continuous treatment in linear models. Stat Med 33:4577-89|
|Joffe, Marshall M; Yang, Wei Peter; Feldman, Harold (2012) G-estimation and artificial censoring: problems, challenges, and applications. Biometrics 68:275-86|
|Joffe, Marshall M (2012) Structural nested models, g-estimation, and the healthy worker effect: the promise (mostly unrealized) and the pitfalls. Epidemiology 23:220-2|
|Joffe, Marshall (2011) Principal stratification and attribution prohibition: good ideas taken too far. Int J Biostat 7:Article 35|