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Published on: July 3, 2020
Improved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with
Anastasios A Tsiatis1, Marie Davidian, Weihua Cao
1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA.
Biometrics
|August 25, 2010
Summary
This study introduces a new doubly robust (DR) estimator for longitudinal data with missing values. The proposed method offers improved performance and robustness against model misspecification in statistical inference.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- Longitudinal data analysis presents challenges with missing data due to dropout.
- Doubly robust (DR) estimators offer consistent inference if either the missingness or data model is correct.
- Existing DR estimators can perform poorly if both models are misspecified.
Purpose of the Study:
- To propose a novel doubly robust (DR) estimator for statistical inference in longitudinal data with monotone missingness.
- To evaluate the performance and robustness of the proposed DR estimator compared to existing methods.
Main Methods:
- Development of a new DR estimator for general monotone coarsening problems.
- Simulation studies to assess estimator performance under various misspecification scenarios.
- Application of the estimator to real-world data from an AIDS clinical trial.
Main Results:
- The proposed DR estimator demonstrated comparable or improved performance over existing DR methods.
- The new estimator showed enhanced robustness even with mild model misspecification.
- Successful application to AIDS clinical trial data, yielding valid statistical inferences.
Conclusions:
- The novel DR estimator is a valuable tool for analyzing longitudinal data with missingness.
- It offers a more reliable approach to statistical inference in the presence of potential model misspecification.
- This method has practical implications for clinical trials and other longitudinal studies.
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