Using Model-Assisted Calibration Methods to Improve Efficiency of Regression Analyses with Two-Phase Samples under Complex Survey Designs

Two-phase sampling designs are frequently employed in epidemiological studies and large-scale health surveys. In such designs, certain variables are exclusively collected within a second-phase random subsample of the initial first-phase sample, often due to factors such as high costs, response burden, or constraints on data collection or measurement assessment. Consequently, second-phase sample estimators can be inefficient due to the diminished sample size. Model-assisted calibration methods have been widely used to improve the efficiency of second-phase estimators. However, none of the existing methods have considered the complexities arising from the intricate sample designs present in both first- and second-phase samples in regression analyses. This paper proposes to calibrate the sample weights for the second-phase subsample to the weighted first-phase sample based on influence functions of regression coefficients for a prediction of the covariate of interest, which can be computed for the entire first-phase sample. We establish the consistency of the proposed calibration estimation and provide variance estimation. Empirical evidence underscores the robustness of calibration on influence functions compared to the imputation method, which can be sensitive to misspecified prediction models for the variable only collected in the second phase. Examples using data from the National Health and Nutrition Examination Survey are provided.