Influence function methods to assess the effectiveness of influenza vaccine with survey data

Objective: To examine a robust relative risk (RR) estimation for survey data analysis with ideal inferential properties under various model assumptions. Data sources: We employed secondary data from the Household Component of the 2000–2016 US Medical Expenditure Panel Survey (MEPS). Study design: We investigate a broad range of data-balancing techniques by implementing influence function (IF) methods, which allows us to easily estimate the variability for the RR estimates in the complex survey setting. We conduct a simulation study of seasonal influenza vaccine effectiveness to evaluate these approaches and discuss techniques that show robust inferential performance across model assumptions. Data collection/Extraction methods: Demographic information, vaccine status, and self-administered questionnaire surveys were obtained from the longitudinal data files. We linked this information with medical condition files and medical event to extract the disease type and associated expenditures for each medical visit. We excluded individuals who were 18 years or younger at the beginning of each panel. Principal findings: Under various model assumptions, the IF methods show robust inferential performance when the data-balancing procedures are incorporated. Once IF methods and data-balancing techniques are implemented, contingency table-based RR estimation yields a comparable result to the generalized linear model approach. We demonstrate the applicability of the proposed methods for complex survey data using 2000–2016 MEPS data. When employing these methods, we find a significant, negative association between vaccine effectiveness (VE) estimates and influenza-incurred expenditures. Conclusions: We describe and demonstrate a robust method for RR estimation and relevant inferences for influenza vaccine effectiveness using MEPS data. The proposed method is flexible and can be extended to weighted data for survey data analysis. Hence, these methods have great potential for health services research, especially when data are nonexperimental and imbalanced.