Utilization of locally estimated scatterplot smoothing (LOESS) regression to estimate missing weights in a longitudinal cohort of breast cancer patients

Introduction

Traditional methods to handle missing data rely on making assumptions about missing data patterns. Locally estimated scatterplot smoothing (LOESS) regression models were explored as a data-driven option to minimize missing weight data in a longitudinal cohort of breast cancer patients.

Methods

Outpatient weights from 2 years prior to breast cancer diagnosis to 10 years post were extracted from electronic health records for 10,778 women with invasive breast cancer diagnosed from 2005‐2013 at Kaiser Permanente. LOESS regression models estimated weights at baseline (breast cancer diagnosis) and 6 follow-up time points (6, 12, 24, 48, 72, and 96 months post-baseline). The weights identified by the LOESS models were compared with those identified by the closest-available method, in which the weight measurement closest to each timepoint within a specified time window was selected.

Results

Compared with the closest-available method, LOESS models identified fewer weights at baseline and 6 months post, but significantly more weights at later follow-up periods. At all timepoints, more than 80% of the weights identified by both approaches differed by 2.50 kilograms or less.

Conclusions

LOESS regression makes effective use of available longitudinal data and may be a beneficial tool to minimize missing longitudinal data in future EHR-based studies.