Doubly robust estimation of partially linear models for longitudinal data with dropouts and measurement error in covariates |
| |
| Authors: | Huiming Lin Jiajia Zhang Wing K. Fung |
| |
| Affiliation: | 1. Department of Biostatistics, School of Public Health and Key Lab of Health Technology Assessment, National Health and Family Planning Commission of the People's Republic of China, Fudan University, Shanghai, People's Republic of China;2. Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, People's Republic of China;3. Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA;4. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, People's Republic of China |
| |
| Abstract: | In longitudinal studies, missing responses and mismeasured covariates are commonly seen due to the data collection process. Without cautiousness in data analysis, inferences from the standard statistical approaches may lead to wrong conclusions. In order to improve the estimation for longitudinal data analysis, a doubly robust estimation method for partially linear models, which can simultaneously account for the missing responses and mismeasured covariates, is proposed. Imprecisions of covariates are corrected by taking advantage of the independence between replicate measurement errors, and missing responses are handled by the doubly robust estimation under the mechanism of missing at random. The asymptotic properties of the proposed estimators are established under regularity conditions, and simulation studies demonstrate desired properties. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study. |
| |
| Keywords: | Doubly robust dropouts measurement error partially linear models |
|
|
