Comparing Missing Data Imputation Methods for Patient-Reported Outcomes in Esophageal Cancer Research
Kweon, Y. J.; Salman, Y.; Dhillon, S.; Dehghani, M.; Mohammed, E. A.; Crump, R. T.
Show abstract
Missing data is common in patient-reported outcomes (PRO) research, particularly in oncology settings. We evaluated common methods for handling missing data in esophageal cancer quality of life measurements, namely: multiple imputation by chained equations, variational autoencoder, denoising autoencoder, Bayesian principal component analysis, a deep autoen-coder method with patient-specific embeddings and temporal pattern modeling, SoftImpute, and K-nearest neighbors. Using data from McGill Universitys Esophageal and Gastric Data- and Bio-Bank, we compared these imputation methods for 44 variables of the Functional Assessment of Cancer Therapy-Esophageal patient-reported outcome measure on execution time, distribution preservation, correlation maintenance, imputation accuracy, and clinical classification performance. Our comprehensive validation framework provides evidence-based recommendations for selecting appropriate imputation methods for esophageal cancer PRO research, which may improve the validity and reliability of research findings in this domain.
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