JMIR Publications

ABSTRACT

Background:

Missing data in electronic health records (EHRs) is highly prevalent and results in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missing-ness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance.

Objective:

We aimed to assess the missing indicator method for longitudinal, repeated measures data using a simulation study mimicking real-world EHR data.

Methods:

We conducted a simulation study aimed to evaluate whether the missing indicator method improved model performance and imputation accuracy for longitudinal data mimicking an application of developing a clinical prediction model for falls in older adults based on EHR data. We simulated a longitudinal binary outcome using mixed effects logistic regression that emulated a falls assessment at annual follow-up visits. Using multivariate imputation by chained equations, we simulated time-invariant predictors such as sex and medical history, as well as dynamic predictors such as physical function, body mass index, and medication use. We induced missing data in predictors under scenarios that had both random (MAR) and dependent missing-ness (MNAR). We evaluated aggregate performance using the area under the curve for models with and without missing indicators as predictors, as well as complete case analysis, across simulation replicates. We evaluated imputation quality using normalized root mean square error for continuous variables, and percent falsely classified for categorical variables.

Results:

Independent of the mechanism used to simulate missing data (MAR or MNAR), overall model performance via area under the curve was similar regardless of whether missing indicators were included in the model. The root mean square error and percent falsely classified measures were similar for models including missing indicators versus those without missing indicators. Model performance and imputation quality were similar regardless of whether the outcome was related to missingness. Imputation with or without missing indicators had similar mean values of area under the curve compared to complete case analysis, although complete case analysis had the largest range of values.

Conclusions:

The results of this study suggest that the inclusion of missing indicators in longitudinal data modeling neither improve nor worsen overall performance or imputation accuracy. Future research is needed to address whether the inclusion of missing indicators is useful in prediction modeling with longitudinal data in different settings, such as high dimensional data analysis.