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Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a
Takuya Kawahara1, Keiko Yamada2,3, Ryohei Terashima4
1Clinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, Japan.
BMJ Open
|December 26, 2022
Summary
Handling missing data in the Geriatric Locomotive Function Scale (GLFS-25) is crucial. Multiple imputation is recommended, but single imputation methods offer negligible bias for up to eight missing items.
Area of Science:
- Gerontology
- Biostatistics
- Rehabilitation Medicine
Background:
- The Geriatric Locomotive Function Scale (GLFS-25) is widely used to assess physical function in older adults.
- Missing data in the GLFS-25 is common, yet optimal methods for handling these missing values remain unclear.
Purpose of the Study:
- To investigate and compare the performance of different statistical methods for handling missing values in the GLFS-25.
- To determine the most effective imputation strategy based on bias and mean squared error.
Main Methods:
- A simulation study was conducted using three distinct datasets representing community dwellers, orthopedic outpatients, and surgical patients.
- Missing data points were artificially introduced into the GLFS-25 across varying percentages (5%-40%) and numbers of missing items (4-16).
- Four imputation methods were evaluated: complete case analysis, multiple imputation, single imputation (mean), and single imputation (domain average).
Main Results:
- Multiple imputation demonstrated the lowest root mean squared error, indicating superior accuracy.
- Complete case analysis exhibited the largest bias.
- Single imputation methods performed intermediately, with absolute bias below 0.1 and comparable results to multiple imputation when missing items were ≤8.
Conclusions:
- Multiple imputation is the preferred method for handling missing data in the GLFS-25.
- Single imputation using individual or domain averages can be considered when the number of missing items is limited (≤8) due to negligible bias.
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