The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Big Data in Organ Transplantation: Registries and Administrative Claims
Open accessAbstract
The field of organ transplantation benefits from large, comprehensive, transplant-specific national data sets available to researchers. In addition to the widely used Organ Procurement and Transplantation Network (OPTN)-based registries (the United Network for Organ Sharing and Scientific Registry of Transplant Recipients data sets) and United States Renal Data System (USRDS) data sets, there are other publicly available national data sets, not specific to transplantation, which have historically been underutilized in the field of transplantation. Of particular interest are the Nationwide Inpatient Sample and State Inpatient Databases, produced by the Agency for Healthcare Research and Quality. The USRDS database provides extensive data relevant to studies of kidney transplantation. Linkage of publicly available data sets to external data sources such as private claims or pharmacy data provides further resources for registry-based research. Although these resources can transcend some limitations of OPTN-based registry data, they come with their own limitations, which must be understood to avoid biased inference. This review discusses different registry-based data sources available in the United States, as well as the proper design and conduct of registry-based research.
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Citations
Cites
D'Agostino, R. B., Grundy, S., Sullivan, L. M., Wilson, P., & for the CHD Risk Prediction Group (2001) · JAMA · doi:10.1001/jama.286.2.180 · Cited by 2,108
A Comprehensive Risk Quantification Score for Deceased Donor Kidneys: The Kidney Donor Risk Index
Rao, P. S., Schaubel, D. E., Guidinger, M. K., Andreoni, K. A., Wolfe, R. A., Merion, R. M., Port, F. K., & Sung, R. S. (2009) · Transplantation · doi:10.1097/tp.0b013e3181ac620b · Cited by 1,079 · cited 2×
“Prediction models Statistical models intended to predict individual outcomes are common in transplantation; examples include the MELD score (47) and the Kidney Donor Risk Index (48).”
BackgroundA critical look at methods for handling missing covariates in epidemiologic regression analyses.
Greenland, S., & Finkle, W. D. (1995) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a117592 · Cited by 891 · cited 1×
“Strategies for dealing with missingness include (in order of robustness): case-wise deletion (complete-case analyses), missing indicator variables and imputation (42).”
Method
Cited by
Organ donation in the US and Europe: The supply vs demand imbalance
Lewis, A., Koukoura, A., Tsianos, G.-I., Gargavanis, A. A., Nielsen, A. A., & Vassiliadis, E. (2021) · Transplantation Reviews · doi:10.1016/j.trre.2020.100585 · Cited by 359
Bae, S., Massie, A. B., Thomas, A. G., Bahn, G., Luo, X., Jackson, K. R., Ottmann, S. E., Brennan, D. C., Desai, N. M., Coresh, J., et al. (2019) · American Journal of Transplantation · doi:10.1111/ajt.14978 · Cited by 138
Self-citationHayes, D., Tobias, J. D., & Tumin, D. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201511-2222oc · Cited by 91
Spector, L. G., Menk, J. S., Vinocur, J. M., Oster, M. E., Harvey, B. A., St. Louis, J. D., Moller, J., & Kochilas, L. K. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.003783 · Cited by 39
Orandi, B. J., Chen, Y., Li, Y., Charyton, D., Lentine, K. L., Lee, B. P., Ali, N., DeMarco, M. P., Weintraub, M. A., Bae, S., et al. (2025) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.0000000750 · Cited by 38
Self-citation
Metadata
- Authors
- Massie, A. B., Kuricka, L. M., & Segev, D. L.
- Venue
- American Journal of Transplantation
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 14
- Issue
- 8
- Pages
- 1723-1730
- ISSN
- 1600-6135 (ISSN-L)
- OpenAlex
- W1511309965
- Cited by
- 382
- References
- 3
- FWCI
- 14.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Massie, A. B., Kuricka, L. M., & Segev, D. L. (2014). Big Data in Organ Transplantation: Registries and Administrative Claims. American Journal of Transplantation. https://doi.org/10.1111/ajt.12777
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.