Multiply Robust Estimation in Regression Analysis With Missing Data
Author:
- Peisong HanPHPeisong Han
- 14 January 2014
- journal article
- theory and-methods
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 109 (507) , 1159-1173
Abstract
Doubly robust estimators are widely used in missing-data analysis. They provide double protection on estimation consistency against model misspecifications. However, they allow only a single model for the missingness mechanism and a single model for the data distribution, and the assumption that one of these two models is correctly specified is restrictive in practice. For regression analysis with possibly missing outcome, we propose an estimation method that allows multiple models for both the missingness mechanism and the data distribution. The resulting estimator is consistent if any one of those multiple models is correctly specified, and thus provides multiple protection on consistency. This estimator is also robust against extreme values of the fitted missingness probability, which, for most doubly robust estimators, can lead to erroneously large inverse probability weights that may jeopardize the numerical performance. The numerical implementation of the proposed method through a modified Newton–Raphson algorithm is discussed. The asymptotic distribution of the resulting estimator is derived, based on which we study the estimation efficiency and provide ways to improve the efficiency. As an application, we analyze the data collected from the AIDS Clinical Trials Group Protocol 175.Keywords
This publication has 50 references indexed in Scilit:
- A further study of the multiply robust estimator in missing data analysisJournal of Statistical Planning and Inference, 2014
- A note on improving the efficiency of inverse probability weighted estimator using the augmentation termStatistics & Probability Letters, 2012
- Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete dataBiometrika, 2009
- Improving Semiparametric Estimation by Using Surrogate DataJournal of the Royal Statistical Society Series B: Statistical Methodology, 2008
- Adjusted Empirical Likelihood and its PropertiesJournal of Computational and Graphical Statistics, 2008
- Doubly Robust Estimation in Missing Data and Causal Inference ModelsBiometrics, 2005
- Semiparametric Estimation of Treatment Effect in a Pretest–Posttest Study with Missing DataStatistical Science, 2005
- Semiparametric efficient estimation for the auxiliary outcome problem with the conditional mean modelCanadian Journal of Statistics, 2004
- Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveysBiometrika, 2002
- A Trial Comparing Nucleoside Monotherapy with Combination Therapy in HIV-Infected Adults with CD4 Cell Counts from 200 to 500 per Cubic MillimeterNew England Journal of Medicine, 1996