Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study
Objectives Despite the possible large number of missing values on the 25-question Geriatric Locomotive Function Scale (GLFS-25), how we should treat them is unknown. In a simulation study, we investigated how to handle missing values in the GLFS-25.Design, setting and participants We used three data...
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BMJ Publishing Group
2022-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/12/e065607.full |
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| author | Sakae Tanaka Keiko Yamada Toru Ogata Hirotaka Chikuda Kozo Nakamura Takuya Kawahara Ryohei Terashima Ikumi Takashima Hiromasa Miura Takashi Ohe |
| author_facet | Sakae Tanaka Keiko Yamada Toru Ogata Hirotaka Chikuda Kozo Nakamura Takuya Kawahara Ryohei Terashima Ikumi Takashima Hiromasa Miura Takashi Ohe |
| author_sort | Sakae Tanaka |
| collection | DOAJ |
| description | Objectives Despite the possible large number of missing values on the 25-question Geriatric Locomotive Function Scale (GLFS-25), how we should treat them is unknown. In a simulation study, we investigated how to handle missing values in the GLFS-25.Design, setting and participants We used three datasets with different participant characteristics: community dwellers who could walk by themselves, outpatients of orthopaedics owing to pain, and patients who required surgery for total knee replacement or lumbar spinal canal stenosis.Outcome measures The missing items of the datasets were artificially created, and four statistical methods, complete case analysis, multiple imputation, single imputation using individual mean, and single imputation using individual domain average, were compared in terms of bias and mean squared error. Simulation studies were conducted to compare them under varying numbers of participants with missing values (5%–40%) and under varying numbers of missing items of GLFS-25 (4–16).Results Multiple imputation had the lowest root mean squared error. Complete case analysis showed the largest bias, and the performances of the single imputation were between those methods. The relative performances were similar across the three datasets. The absolute bias of the single imputation was<0.1. The bias and mean squared error of multiple imputation and single imputation were comparable when the number of missing items was less than or equal to eight.Conclusions Multiple imputation is preferable, although single imputation using subject average/subject domain average can be used with practically negligible bias as long as the number of missing items is up to 8 out of 25 items in each individual of the population. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2022-12-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-98d2283cb4524da5be3c55b0b0213ec52025-08-20T02:16:01ZengBMJ Publishing GroupBMJ Open2044-60552022-12-01121210.1136/bmjopen-2022-065607Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation studySakae Tanaka0Keiko Yamada1Toru Ogata2Hirotaka Chikuda3Kozo Nakamura4Takuya Kawahara5Ryohei Terashima6Ikumi Takashima7Hiromasa Miura8Takashi Ohe96 Department of Orthopedic Surgery, Faculty of Medicine, The University of Tokyo, Tokyo, Japan1 Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan6 Center for Sport Science and Health Promotion, Department of Rehabilitaion for the Movement Functions, National Rehabilitation Center for Persons with Disabilities, Tokorozawa, Japan1 Orthopaedic Surgery, Gunma University Graduate School of Medicine School of Medicine Faculty of Medicine, Gunma, JapanDepartment of Orthopaedic Surgery, The University of Tokyo, Tokyo, JapanClinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, JapanClinical and Translational Research Center, Niigata University Medical and Dental Hospital, Niigata, JapanClinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, JapanDepartment of Bone and Joint Surgery, Ehime University, Ehime, JapanDepartment of Orthopaedic Surgery, NTT Medical Center Tokyo, Tokyo, JapanObjectives Despite the possible large number of missing values on the 25-question Geriatric Locomotive Function Scale (GLFS-25), how we should treat them is unknown. In a simulation study, we investigated how to handle missing values in the GLFS-25.Design, setting and participants We used three datasets with different participant characteristics: community dwellers who could walk by themselves, outpatients of orthopaedics owing to pain, and patients who required surgery for total knee replacement or lumbar spinal canal stenosis.Outcome measures The missing items of the datasets were artificially created, and four statistical methods, complete case analysis, multiple imputation, single imputation using individual mean, and single imputation using individual domain average, were compared in terms of bias and mean squared error. Simulation studies were conducted to compare them under varying numbers of participants with missing values (5%–40%) and under varying numbers of missing items of GLFS-25 (4–16).Results Multiple imputation had the lowest root mean squared error. Complete case analysis showed the largest bias, and the performances of the single imputation were between those methods. The relative performances were similar across the three datasets. The absolute bias of the single imputation was<0.1. The bias and mean squared error of multiple imputation and single imputation were comparable when the number of missing items was less than or equal to eight.Conclusions Multiple imputation is preferable, although single imputation using subject average/subject domain average can be used with practically negligible bias as long as the number of missing items is up to 8 out of 25 items in each individual of the population.https://bmjopen.bmj.com/content/12/12/e065607.full |
| spellingShingle | Sakae Tanaka Keiko Yamada Toru Ogata Hirotaka Chikuda Kozo Nakamura Takuya Kawahara Ryohei Terashima Ikumi Takashima Hiromasa Miura Takashi Ohe Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study BMJ Open |
| title | Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study |
| title_full | Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study |
| title_fullStr | Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study |
| title_full_unstemmed | Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study |
| title_short | Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study |
| title_sort | practical guidance to handle missing values in the 25 question geriatric locomotive function scale glfs 25 a simulation study |
| url | https://bmjopen.bmj.com/content/12/12/e065607.full |
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