Informative missingness in electronic health record systems: the curse of knowing
Abstract Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical...
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| Format: | Article |
| Language: | English |
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BMC
2020-07-01
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| Series: | Diagnostic and Prognostic Research |
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| Online Access: | http://link.springer.com/article/10.1186/s41512-020-00077-0 |
| _version_ | 1818190709540257792 |
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| author | Rolf H. H. Groenwold |
| author_facet | Rolf H. H. Groenwold |
| author_sort | Rolf H. H. Groenwold |
| collection | DOAJ |
| description | Abstract Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example by including a separate category of a predictor variable that has missing values. The predictive performance of such a model depends on the transportability of the missing data mechanism, which may be compromised once the model is deployed in practice and the predictive value of certain variables becomes known. Using synthetic data, this phenomenon is explained and illustrated. |
| first_indexed | 2024-12-12T00:03:01Z |
| format | Article |
| id | doaj.art-a309e76703cb468ab77e85559b905fa5 |
| institution | Directory Open Access Journal |
| issn | 2397-7523 |
| language | English |
| last_indexed | 2024-12-12T00:03:01Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic and Prognostic Research |
| spelling | doaj.art-a309e76703cb468ab77e85559b905fa52022-12-22T00:45:11ZengBMCDiagnostic and Prognostic Research2397-75232020-07-01411610.1186/s41512-020-00077-0Informative missingness in electronic health record systems: the curse of knowingRolf H. H. Groenwold0Department of Clinical Epidemiology, Leiden University Medical CentreAbstract Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example by including a separate category of a predictor variable that has missing values. The predictive performance of such a model depends on the transportability of the missing data mechanism, which may be compromised once the model is deployed in practice and the predictive value of certain variables becomes known. Using synthetic data, this phenomenon is explained and illustrated.http://link.springer.com/article/10.1186/s41512-020-00077-0Prediction modellingMissing dataRoutine care data |
| spellingShingle | Rolf H. H. Groenwold Informative missingness in electronic health record systems: the curse of knowing Diagnostic and Prognostic Research Prediction modelling Missing data Routine care data |
| title | Informative missingness in electronic health record systems: the curse of knowing |
| title_full | Informative missingness in electronic health record systems: the curse of knowing |
| title_fullStr | Informative missingness in electronic health record systems: the curse of knowing |
| title_full_unstemmed | Informative missingness in electronic health record systems: the curse of knowing |
| title_short | Informative missingness in electronic health record systems: the curse of knowing |
| title_sort | informative missingness in electronic health record systems the curse of knowing |
| topic | Prediction modelling Missing data Routine care data |
| url | http://link.springer.com/article/10.1186/s41512-020-00077-0 |
| work_keys_str_mv | AT rolfhhgroenwold informativemissingnessinelectronichealthrecordsystemsthecurseofknowing |