The Dual-Focus Dynamic Multiple Imputation Approach For MNAR Missing Values In Medical Data

Missing value imputation in medical datasets is an important research topic. Most studies assume that missing values are Missing at Random (MAR), but verifying whether data are MAR or Missing Not at Random (MNAR) is challenging because it is impossible to evaluate whether the unobserved data is related to the missing data. Besides, the possible evaluation method sensitivity analysis has many limitations and shortcomings. Therefore, considering the extreme complexity of the human body, treating missing values as MNAR is preferable. Existing MNAR imputation methods require assumptions about the distribution of unobserved variables and establish joint probabilities, so they rely on experience and may lead to bias. In addition, these methods also struggle with complex relationships and distributions. To address these problems, this paper proposes the Dual-Focus Dynamic Multiple Imputation (DDMI) model for MNAR missing values in medical data. The DDMI uses piece-wise approximation to decompose complex relationships and directly calculates the impact of unobserved variables on patient indicators, avoiding distribution assumptions. In addition, the DDMI captures both population and individual-level information to predict missing values and then refines the results through multiple iterations, and the original information is combined in each iteration to mitigate information loss and improve convergence. We test DDMI on two real-world datasets. Results show that DDMI outperforms other methods.