The K Nearest Neighbor Algorithm for Imputation of Missing Longitudinal Prenatal Alcohol Data

Abstract

Background: Missing data are a source of bias in many epidemiologic studies. This is problematic in alcohol research where data missingness is linked to patterns of drinking behavior.

Methods: The Safe Passage Study was a prospective investigation of prenatal alcohol consumption and fetal/infant outcomes (n=11,083). Daily alcohol consumption for the last reported drinking day and 30 days prior was recorded using the Timeline Follow back method. Of the 3.2 million person-days of observation, data were missing for 0.36 million (11.4%). We imputed missing exposure data using a machine learning algorithm; 'K Nearest Neighbor' (k-NN). k-NN imputes missing values for a participant using data of other participants closest to it. Imputed values were weighted for the distances from nearest neighbors and matched for the day of the week. Validation was done on 500 iterations after randomly deleting data for 5-15 consecutive days from first trimester.

Results: We found that data from 5 nearest neighbors (i.e., K=5) and segments of 55 days provided imputed values with least imputation error. After deleting data segments from a first trimester data set with no missing days, there was no difference between actual and predicted values for 64% of deleted segments. For 31% of the segments, imputed data were within +/-1 drink/day of the actual.

Conclusions: k-NN can be used to impute missing data from longitudinal studies of alcohol during pregnancy with high accuracy.

Note:
Funding Information: This research was supported by grants UH3OD023279, U01HD055154, U01HD045935, U01HD055155, and U01AA016501, issued by the Office of the Director, National Institutes of Health of the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Institute on Deafness and Other Communication Disorders. Ayesha Sania is supported by UH3OD023279-05S1, re-entry supplement from Office of the Director, NIH, and Office of Research on Women Health (ORWH).

Declaration of Interests: The authors declare that they have no competing interests.

Ethics Approval Statement: Ethical approval was obtained for each participating PASS network site from their institutional review boards including Stellenbosch University, Sanford Health, the Indian Health Service and from participating Tribal Nations. Written informed consent was obtained from all participants.

Keywords: k Nearest Neighbor, k-NN, Machine Learning, Data Missingness, Data Imputation, Prenatal Alcohol Consumption, Longitudinal Alcohol Consumption