3 papers
stat.ME2026
Addressing errors in multiple variables using generalized raking and cumulative probability models
Eric S. Kawaguchi, Chun Li, Frank E. Harrell +3
Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study finding…
stat.ME2026
Generalized raking and stabilized weights for regression modeling in two-phase samples
Tong Chen, Joshua Slone, Gustavo Amorim +3
In regression models fitted to data from complex survey designs, sampling weights often incorporate non-essential variation, inflating variance estimates. Stabilized weights mitiga…
stat.ME2026
Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems
Sky Qiu, Susan Gruber, Pamela A. Shaw +2
In a typical two-phase design, a random sample is drawn from the target population in phase 1, during which only a subset of variables is collected. In phase 2, a subsample of the…