Improved double-robust estimation in missing data and causal inference models
Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory. Copyright 2012, Oxford University Press.
Year of publication: |
2012
|
---|---|
Authors: | Rotnitzky, Andrea ; Lei, Quanhong ; Sued, Mariela ; Robins, James M. |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 99.2012, 2, p. 439-456
|
Publisher: |
Biometrika Trust |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Inference in Randomized Studies with Informative Censoring and Discrete Time-to-Event Endpoints
Scharfstein, Daniel, (2001)
-
Efficiency Comparisons in Multivariate Multiple Regression with Missing Outcomes
Rotnitzky, Andrea, (1997)
-
Theory and Methods - Comment - On Profile Likelihood
Robins, James M., (2000)
- More ...