TY  - JOUR
PY  - 2020//
TI  - Imputation of ordinal outcomes: a comparison of approaches in traumatic brain injury
JO  - Journal of neurotrauma
A1  - Kunzmann, Kevin
A1  - Wernisch, Lorenz
A1  - Richardson, Sylvia
A1  - Steyerberg, Ewout W.
A1  - Lingsma, Hester F.
A1  - Ercole, Ari
A1  - Maas, Andrew
A1  - Menon, David
A1  - Wilson, Lindsay
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0897-7151
UR  - http://dx.doi.org/10.1089/neu.2019.6858
ID  - ref1
ER  -