Missing data are almost inevitable in medical research. This leads to a loss of power and potential bias. Multiple imputation is a widely-used and flexible approach for handling missing data.

Missing Data Analysis and Multiple Imputation Methods Publication Trend The graph below shows the total number of publications each year in Missing Data Analysis and Multiple Imputation Methods.

Missing data are almost inevitable in medical research. This leads to a loss of power and potential bias. Multiple imputation is a widely-used and flexible approach for handling missing data.

Angelina Hammon, Sabine Zinn, Multiple imputation of binary multilevel missing not at random data, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 69, No. 3 (2020), pp.

Next, we will briefly cover early methods for handling missing data, such as complete case analysis and single imputation techniques (mean, hot deck, etc.), and why in practice they can produce ...

Software for multiple imputation still struggles with the largest and most complicated data sets. But new multiple-imputation software that uses machine learning has been able to impute more ...

The National Health Interview Survey (NHIS) provides a rich source of data for studying relationships between income and health and for monitoring health and health care for persons at different ...

When the degree of missingness in the data raises questions about the validity of preferred methods such as model-based multiple imputation, they recommend that sensitivity analyses be performed ...

Imputation of missing ESG data using deep latent variable models December 4, 2020 In finance, data is often incomplete because the data is unavailable, inapplicable or unreported.