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title = {A {Harmonized} {Data} {Quality} {Assessment} {Terminology} and {Framework} for the {Secondary} {Use} of {Electronic} {Health} {Record} {Data}},
volume = {4},
issn = {2327-9214},
doi = {10.13063/2327-9214.1244},
language = {eng},
number = {1},
journal = {EGEMS (Washington, DC)},
author = {Kahn, Michael G. and Callahan, Tiffany J. and Barnard, Juliana and Bauck, Alan E. and Brown, Jeff and Davidson, Bruce N. and Estiri, Hossein and Goerg, Carsten and Holve, Erin and Johnson, Steven G. and Liaw, Siaw-Teng and Hamilton-Lopez, Marianne and Meeker, Daniella and Ong, Toan C. and Ryan, P. B. and Shang, Ning and Weiskopf, Nicole G. and Weng, Chunhua and Zozus, Meredith N. and Schilling, Lisa},
year = {2016},
pmid = {27713905},
pmcid = {PMC5051581},
keywords = {data completeness, data use \& quality, electronic health records},
pages = {1244}
}
@article{huser_methods_2018,
title = {Methods for examining data quality in healthcare integrated data repositories},
volume = {23},
issn = {2335-6936},
language = {eng},
journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
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pmid = {29218922},
keywords = {Computational Biology, Computer Systems, Consumer Product Safety, Data Accuracy, Electronic Health Records, Humans, Sentinel Surveillance, United States},
pages = {628--633}
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@article{kahn_transparent_2015,
title = {Transparent reporting of data quality in distributed data networks},
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language = {eng},
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journal = {EGEMS (Washington, DC)},
author = {Kahn, Michael G. and Brown, Jeffrey S. and Chun, Alein T. and Davidson, Bruce N. and Meeker, Daniella and Ryan, P. B. and Schilling, Lisa M. and Weiskopf, Nicole G. and Williams, Andrew E. and Zozus, Meredith Nahm},
year = {2015},
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keywords = {comparative effectiveness, data use and quality, Informatics, observational data, research networks},
pages = {1052}
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title = {Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research},
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keywords = {Data Collection, Electronic Health Records, Humans, Information Dissemination, Quality Control, Reproducibility of Results, Research Design},
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}
@article{moons_transparent_2015,
title = {Transparent {Reporting} of a multivariable prediction model for {Individual} {Prognosis} or {Diagnosis} ({TRIPOD}): explanation and elaboration},
volume = {162},
issn = {1539-3704},
shorttitle = {Transparent {Reporting} of a multivariable prediction model for {Individual} {Prognosis} or {Diagnosis} ({TRIPOD})},
doi = {10.7326/M14-0698},
abstract = {The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.},
language = {eng},
number = {1},
journal = {Annals of Internal Medicine},
author = {Moons, Karel G. M. and Altman, Douglas G. and Reitsma, Johannes B. and Ioannidis, John P. A. and Macaskill, Petra and Steyerberg, Ewout W. and Vickers, Andrew J. and Ransohoff, David F. and Collins, Gary S.},
month = jan,
year = {2015},
pmid = {25560730},
keywords = {Humans, Reproducibility of Results, prognosis, Multivariate Analysis, models, statistical, Diagnosis, Checklist, Decision Support Techniques, Guidelines as Topic, Publishing},
pages = {W1--73}
}
@article{tibshirani_regression_1996,
title = {Regression {Shrinkage} and {Selection} via the {Lasso}},
volume = {58},
issn = {0035-9246},
url = {https://www.jstor.org/stable/2346178},
abstract = {We propose a new method for estimation in linear models. The `lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.},
number = {1},
urldate = {2019-07-09},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
author = {Tibshirani, Robert},
year = {1996},
pages = {267--288}
}
@article{cutrona_validation_2013,
title = {Validation of acute myocardial infarction in the {Food} and {Drug} {Administration}'s {Mini}-{Sentinel} program},
volume = {22},
issn = {1099-1557},
doi = {10.1002/pds.3310},
abstract = {PURPOSE: To validate an algorithm based upon International Classification of Diseases, 9(th) revision, Clinical Modification (ICD-9-CM) codes for acute myocardial infarction (AMI) documented within the Mini-Sentinel Distributed Database (MSDD).
METHODS: Using an ICD-9-CM-based algorithm (hospitalized patients with 410.x0 or 410.x1 in primary position), we identified a random sample of potential cases of AMI in 2009 from four Data Partners participating in the Mini-Sentinel Program. Cardiologist reviewers used information abstracted from hospital records to assess the likelihood of an AMI diagnosis based on criteria from the Joint European Society of Cardiology and American College of Cardiology Global Task Force. Positive predictive values (PPVs) of the ICD-9-based algorithm were calculated.
RESULTS: Of the 153 potential cases of AMI identified, hospital records for 143 (93\%) were retrieved and abstracted. Overall, the PPV was 86.0\% (95\% confidence interval; 79.2\%, 91.2\%). PPVs ranged from 76.3\% to 94.3\% across the four Data Partners.
CONCLUSIONS: The overall PPV of potential AMI cases, as identified using an ICD-9-CM-based algorithm, may be acceptable for safety surveillance; however, PPVs do vary across Data Partners. This validation effort provides a contemporary estimate of the reliability of this algorithm for use in future surveillance efforts conducted using the Food and Drug Administration's MSDD.},
language = {eng},
number = {1},
journal = {Pharmacoepidemiology and Drug Safety},
author = {Cutrona, Sarah L. and Toh, Sengwee and Iyer, Aarthi and Foy, Sarah and Daniel, Gregory W. and Nair, Vinit P. and Ng, Daniel and Butler, Melissa G. and Boudreau, Denise and Forrow, Susan and Goldberg, Robert and Gore, Joel and McManus, David and Racoosin, Judith A. and Gurwitz, Jerry H.},