10 Imputation and missing indicators for handling longitudinal data with nonignorable missingness: A simulation study based on electronic health record data

Oregon Convention Center 

Nonignorable missing data occurs when missing values are associated with an outcome of interest. For example, in electronic health record data, a laboratory variable may be missing because a patient was too sick for it to be measured. A simple method for handling nonignorable missing data is to include indicator variables for whether a value is missing, known as the missing indicator method. To date, there is little guidance about using the missing indicator method for longitudinal data with nonignorable missing values. We conduct a simulation study to investigate whether the missing indicator method is beneficial for imputing and modeling longitudinal data with nonignorable missingness. Using simulated data that mimic electronic health record data for repeated measures of falls in older adults, we found that including missing indicators in imputation or modeling did not substantially impact the accuracy of imputations; however, use of missing indicators resulted in slightly higher area under the receiver operating curve (0.921) compared to models without missing indicators (0.886) when averaged across the simulation runs.