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Abstract This article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete‐case approach to estimation and inference is investigated. This approach modifies IPW complete‐case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase‐one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite‐sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV‐1 vaccine efficacy trial to investigate vaccine‐induced IgG binding antibodies to HIV‐1 as correlates of acquisition of HIV‐1 infection while taking account of whether the HIV‐1 sequences are near or far from the HIV‐1 sequences represented in the vaccine construct.
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Robust estimation for linear panel data models
In different fields of applications including, but not limited to, behavioral, environmental, medical sciences, and econometrics, the use of panel data regression models has become increasingly popular as a general framework for making meaningful statistical inferences. However, when the ordinary least squares (OLS) method is used to estimate the model parameters, presence of outliers may significantly alter the adequacy of such models by producing biased and inefficient estimates. In this work, we propose a new, weighted likelihood based robust estimation procedure for linear panel data models with fixed and random effects. The finite sample performances of the proposed estimators have been illustrated through an extensive simulation study as well as with an application to blood pressure dataset. Our thorough study demonstrates that the proposed estimators show significantly better performances over the traditional methods in the presence of outliers and produce competitive results to the OLS based estimates when no outliers are present in the dataset.
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- Award ID(s):
- 1811384
- PAR ID:
- 10455875
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Statistics in Medicine
- Volume:
- 39
- Issue:
- 29
- ISSN:
- 0277-6715
- Format(s):
- Medium: X Size: p. 4421-4438
- Size(s):
- p. 4421-4438
- Sponsoring Org:
- National Science Foundation
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