Generative adversarial networks for imputing missing data for big data clinical research

Abstract

Background: Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets.

Objectives: This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest.

Methods: Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test.

Results: Both missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000.

Conclusion: GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research.

Trial registration: ClinicalTrials.gov ID: NCT03299010 ; Unique Protocol ID: HKUCTR-2232.

Keywords: Big data; Clinical research; Generative adversarial network; Machine learning; Missing data imputation.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1

Density plots displaying the distribution of the absolute difference between imputed values and true values on continuous variables by different methods (missingness rate = 50%). (Note: a and b are representative continuous variables in DM-data, c and d are representative continuous variables in HT-data)

Fig. 2

Bar plots displaying the distribution of imputed allocation of categorical variables by different methods (missingness rate = 50%). (Note: a and b are representative continuous variables in DM-data, c and d are representative continuous variables in HT-data; Shaded areas indicate the proportion that correctly imputed in each category by each method)

Fig. 3

Computation time of one imputation process by each method on DM-data. a Computation time on PC; b Computation time on HPC