Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

Abstract Background Surveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to th...

Full description

Bibliographic Details
Main Authors: Luis Castro, María del Mar Rueda, Carmen Sánchez-Cantalejo, Ramón Ferri, Andrés Cabrera-León
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Research Methodology
Subjects:
Public health
COVID-19
Panel surveys
Sampling
Machine learning
Non-response bias
Online Access:https://doi.org/10.1186/s12874-024-02171-z

Internet

https://doi.org/10.1186/s12874-024-02171-z

Similar Items