A multiple imputation-based sensitivity analysis approach for regression analysis with a missing not at random covariate.

ABSTRACT: Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness mechanism is unverifiable based on observed data. If there is a suspicion of missing not at random (MNAR), researchers often perform sensitivity analysis to evaluate the impact of various missingness mechanisms. Under the selection modeling framework, we propose a sensitivity analysis approach with a standardized sensitivity parameter using a nonparametric multiple imputation strategy. The proposed approach requires fitting two working models to derive two predictive scores: one for predicting missing covariate values and the other for pr

SUBMITTER: Hsu CH 

PROVIDER: S-EPMC10718304 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature