Benchmarking Machine Learning Missing Data Imputation Methods in Large-Scale Mental Health Survey Databases

Databases with mental and behavioral health surveys suffer from missingness when participants skip the entire survey, affecting the data quality and sample size. We investigated the missing data patterns and evaluate the imputation performance in Simons Powering Autism Research (SPARK), a large-scale autism cohort consists of over 117,000 participants. Four common methods were assessed: Multiple Imputation by Chained Equations (MICE), K-Nearest Neighbors (KNN), MissForest, and Multiple Imputation with Denoising Autoencoders (MIDAS). In a complete subset of 15,196 autism participants, we simulated three types of missingness patterns. We observed that MIDAS and KNN performed the best as the rate of random missingness increased and when blockwise missingness was simulated. The average computational times for MIDAS and KNN were 10 minutes, 35 minutes for MissForest, and 290 minutes for MICE. MIDAS and KNN both provide promising imputation performance in mental and behavioral health survey data that exhibit blockwise missingness patterns. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: SFARI Base (https://base.sfari.org) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes SPARK Phenotype Dataset is accessible through application at SFARI Base.