Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review
ABSTRACT Understanding is limited regarding strategies for addressing missing value when developing and validating models to predict cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). This study aimed to investigate the presence of and approaches to missing data in these prediction mod...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Diabetes |
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| Online Access: | https://doi.org/10.1111/1753-0407.70049 |
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| _version_ | 1832583836522774528 |
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| author | Wenhui Ren Keyu Fan Zheng Liu Yanqiu Wu Haiyan An Huixin Liu |
| author_facet | Wenhui Ren Keyu Fan Zheng Liu Yanqiu Wu Haiyan An Huixin Liu |
| author_sort | Wenhui Ren |
| collection | DOAJ |
| description | ABSTRACT Understanding is limited regarding strategies for addressing missing value when developing and validating models to predict cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). This study aimed to investigate the presence of and approaches to missing data in these prediction models. The MEDLINE electronic database was systematically searched for English‐language studies from inception to June 30, 2024. The percentages of missing values, missingness mechanisms, and missing data handling strategies in the included studies were extracted and summarized. This study included 51 articles published between 2001 and 2024, involving 19 studies that focused solely on prediction model development, and 16 and 16 studies that incorporated internal and external validation, respectively. Most articles reported missing data in the development (n = 40/51) and external validation (n = 12/16) stages. Furthermore, the missing data were addressed in 74.5% of development studies and 68.8% of validation studies. Imputation emerged as the predominant method employed for both development (27/40) and validation (7/12) purposes, followed by deletion (17/40 and 4/12, respectively). During the model development phase, the number of studies reported missing data increased from 9 out of 15 before 2016 to 31 out of 36 in 2016 and subsequent years. Although missing values have received much attention in CVD risk prediction models in patients with T2DM, most studies lack adequate reporting on the methodologies used for addressing the missing data. Enhancing the quality assurance of prediction models necessitates heightened clarity and the utilization of suitable methodologies to handle missing data effectively. |
| format | Article |
| id | doaj-art-26b27699b249421093f271ebfabffa2d |
| institution | Kabale University |
| issn | 1753-0393 1753-0407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes |
| spelling | doaj-art-26b27699b249421093f271ebfabffa2d2025-01-28T04:44:58ZengWileyJournal of Diabetes1753-03931753-04072025-01-01171n/an/a10.1111/1753-0407.70049Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic ReviewWenhui Ren0Keyu Fan1Zheng Liu2Yanqiu Wu3Haiyan An4Huixin Liu5Department of Clinical Epidemiology and Biostatistics Peking University People's Hospital Beijing ChinaDepartment of Anesthesiology Peking University People's Hospital Beijing ChinaDepartment of Clinical Epidemiology and Biostatistics Peking University People's Hospital Beijing ChinaDepartment of Clinical Epidemiology and Biostatistics Peking University People's Hospital Beijing ChinaDepartment of Anesthesiology Peking University People's Hospital Beijing ChinaDepartment of Clinical Epidemiology and Biostatistics Peking University People's Hospital Beijing ChinaABSTRACT Understanding is limited regarding strategies for addressing missing value when developing and validating models to predict cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). This study aimed to investigate the presence of and approaches to missing data in these prediction models. The MEDLINE electronic database was systematically searched for English‐language studies from inception to June 30, 2024. The percentages of missing values, missingness mechanisms, and missing data handling strategies in the included studies were extracted and summarized. This study included 51 articles published between 2001 and 2024, involving 19 studies that focused solely on prediction model development, and 16 and 16 studies that incorporated internal and external validation, respectively. Most articles reported missing data in the development (n = 40/51) and external validation (n = 12/16) stages. Furthermore, the missing data were addressed in 74.5% of development studies and 68.8% of validation studies. Imputation emerged as the predominant method employed for both development (27/40) and validation (7/12) purposes, followed by deletion (17/40 and 4/12, respectively). During the model development phase, the number of studies reported missing data increased from 9 out of 15 before 2016 to 31 out of 36 in 2016 and subsequent years. Although missing values have received much attention in CVD risk prediction models in patients with T2DM, most studies lack adequate reporting on the methodologies used for addressing the missing data. Enhancing the quality assurance of prediction models necessitates heightened clarity and the utilization of suitable methodologies to handle missing data effectively.https://doi.org/10.1111/1753-0407.70049cardiovascular diseasesdata handlingrisk assessmentstatistical data interpretationstatistical modeltype 2 diabetes mellitus |
| spellingShingle | Wenhui Ren Keyu Fan Zheng Liu Yanqiu Wu Haiyan An Huixin Liu Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review Journal of Diabetes cardiovascular diseases data handling risk assessment statistical data interpretation statistical model type 2 diabetes mellitus |
| title | Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review |
| title_full | Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review |
| title_fullStr | Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review |
| title_full_unstemmed | Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review |
| title_short | Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review |
| title_sort | overcoming missing data accurately predicting cardiovascular risk in type 2 diabetes a systematic review |
| topic | cardiovascular diseases data handling risk assessment statistical data interpretation statistical model type 2 diabetes mellitus |
| url | https://doi.org/10.1111/1753-0407.70049 |
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