Citation

Kunzmann K, Wernisch L, Richardson S, Steyerberg EW, Lingsma HF, Ercole A, Maas A, Menon D, Wilson L. J. Neurotrauma 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2019.6858

PMID

33108942

Abstract

Loss to follow-up and missing outcomes data are important issues for longitudinal observational studies and clinical trials in traumatic brain injury. One popular solution to missing 6-month outcomes has been to use the last observation carry forward (LOCF). The purpose of the current study was to compare the performance of model-based single-imputation methods with that of the LOCF approach. We hypothesized that model-based methods would perform better as they potentially make better use of available outcome data. The CENTER-TBI study (n = 4509) included longitudinal outcome collection at 2 weeks, 3 months, 6 months, and 12 months post injury; a total of 8185 GOSe observations were included in the database. We compared imputation of 6-month outcomes using LOCF, a mixed effect model, a Gaussian process regression, and a multi-state model. Model performance was assessed via cross-validation on the subset of individuals with a valid GOSe value within 180 +/- 14 days post-injury (n = 1083). All models were fit on the entire available data after removing the 180 +/- 14 days post-injury observations from the respective test fold. The LOCF method showed lower accuracy (i.e. poorer agreement between imputed and observed values) than model-based methods of imputation, and showed a strong negative bias (i.e. it imputed lower than observed outcomes). Accuracy and bias for the three model-based approaches were similar to one another, with the multi-state model having the best overall performance. All methods of imputation showed variation across different outcome categories, with better performance for more frequent outcomes. We conclude that model-based methods of single imputation have substantial performance advantages over LOCF in addition to providing more complete outcome data.

Language: en

Keywords

EPIDEMIOLOGY; TRAUMATIC BRAIN INJURY; OUTCOME MEASURES