Using machine-learning algorithms to improve imputation in the medical expenditure panel survey.

ABSTRACT:

Objective

To assess the feasibility of applying machine learning (ML) methods to imputation in the Medical Expenditure Panel Survey (MEPS).

Data sources

All data come from the 2016-2017 MEPS.

Study design

Currently, expenditures for medical encounters in the MEPS are imputed with a predictive mean matching (PMM) algorithm in which a linear regression model is used to predict expenditures for events with (donors) and without (recipients) data. Recipient events and donor events are then matched based on the smallest distance between predicted expenditures, and the donor event's expenditures are used as the recipient event's imputation. We replace linear regression algorithm in the PMM framework with ML methods to predict expenditures. We examine five alternatives to lin

SUBMITTER: McClellan C 

PROVIDER: S-EPMC10012220 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature