Data · dataset · 2016
Data from: Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease
Listed in DataCite
This paper presents a detailed survival analysis for chronic kidney disease (CKD).
Description
The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests.
Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.
Links
Where it is published
- Repository landing page wiley.figshare.com/articles/dataset/Survival_Analysis_with_Electronic_Health_Reco… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.1514868 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Zero v1.0 Universal creativecommons.org/publicdomain/zero/1.0/legalcode ↗
license · from DataCite
- IsSupplementTo 10.1002/(issn)1932-1872 doi.org/10.1002/(issn)1932-1872 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.1514868 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.1514868 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Mathematics
- From keywords
- Mathematics & Statistics · Statistics
- Inferred from text
- Disease 75% · Longitudinal study 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.1514868 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| byte_size | source · DataCite | connector:datacite@1.0.0 | |
| concepts[disease].local:disease:disease | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].fos:mathematics | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · DataCite | vocabulary-mapper@1.0.0 | keywords['Statistics'] |
| concepts[method].local:method:longitudinal-study | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · DataCite | connector:datacite@1.0.0 | |
| description | source · DataCite | connector:datacite@1.0.0 | /data/attributes/descriptions |
| license | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| publication_date | source · DataCite | connector:datacite@1.0.0 | /data/attributes/dates |
| title | source · DataCite | connector:datacite@1.0.0 | /data/attributes/titles/0/title |
| updated_date | source · DataCite | connector:datacite@1.0.0 |