Multiple imputation of missing values in a cancer mortality analysis with estimated exposure dose
Imputation of missing values in a cancer mortality analysis in relation to estimated dose of dioxin for a cohort of chemical workers is considered. In particular, some subjects of the cohort have the body mass index (BMI) missing. This quantity is an essential ingredient for a toxicokinetic model that gives the estimated absorbed dose, which is then used for risk estimation in a proportional hazards model. Imputation of BMI allows to recover information and to use the entire cohort for risk estimation. Both conditional mean imputation and multiple imputation are used. The latter is a simulation-based approach to the analysis of missing data which takes into account the uncertainty of the imputation process using several imputations for each missing value. In the present context, the two imputation methods gave similar results, both correcting for bias (although with some questions) and leading to increased efficiency with respect to the complete-case analysis that simply discards the partially unobserved individuals.
multiple imputation missing value dioxin toxicokinetic model proportional hazards model markov chain monte carlo